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1pen textbooks *and* a culture of lengthy optional reading lists, which contributes to OER monograph adoption.\\n\\n![](./img/AUX25.png) \\n\\nMost European countries resemble Australia in their overall lower rates of adoption and heavy use of monographs -- but we are less confident in our tracking outside of anglophone-country curricula. There are other sources of possible error.  Probably the most consequential is the undercount of OpenStax titles, which are routinely attributed on syllabi to the publisher rather than to what is often a group of authors.  Better accounting for bad OpenStax citations might add half a percentage point to US counts -- significant, but not interpretation changing. We\'re generally reluctant to create bespoke rules for publishers, but may make an exception in this case."},{"id":"Spring-2026-Update","metadata":{"permalink":"/Spring-2026-Update","source":"@site/blog/2026-05-22-Spring-26-update.md","title":"Spring 2026 Update","description":"We\'ve just released Version 2.14 of the syllabus dataset, which adds 5.2 million syllabi to the collection and brings the topline numbers to:","date":"2026-05-22T00:00:00.000Z","formattedDate":"May 22, 2026","tags":[{"label":"opensyllabus","permalink":"/tags/opensyllabus"}],"readingTime":2.855,"hasTruncateMarker":false,"authors":[{"name":"Joe Karaganis","url":"https://github.com/karaganis","imageURL":"https://avatars.githubusercontent.com/u/5721537?v=4","key":"joe"}],"frontMatter":{"slug":"Spring-2026-Update","title":"Spring 2026 Update","authors":["joe"],"tags":["opensyllabus"]},"prevItem":{"title":"OER Update for 2025","permalink":"/OER-Update-for-2025"},"nextItem":{"title":"OER Marches On in 2024","permalink":"/OER-Update-for-2024"}},"content":"We\'ve just released Version 2.14 of the syllabus dataset, which adds 5.2 million syllabi to the collection and brings the topline numbers to:\\n\\n- 32.9 million syllabi \\n- 8620 colleges and universities represented.\\n- 156 schools with over 50,000 syllabi.\\n- 94 million citations of\\n- 4.4 million unique titles\\n\\nFor comparison, our 2.10 dataset, release 2 1/2 years ago, topped out at:\\n\\n- 18.9 million syllabi\\n- 7900 schools\\n- 75 with over 50,000 syllabi\\n- 56 million citations of \\n- 3.4 million titles \\n\\nNew versions of the dataset usually include more syllabi collected, more discoverable titles, better deduplication, and better overall quality of the extracted sections.  This is a big part of the work of building and curating the largest archive of teaching materials in the world.  Syllabus numbers grow as new terms become available, but also as we intermittently revisit the roughly 10,000 schools we track over time. We also invest in collecting from older sources, which slowly expands the historical dimension of the collection. In practice, this means that the collection peak is always several years behind the current year. Currently, that peak is 2021. Taken together, these factors produce a \'crawl frontier\' that traces the the growth of the collection over time.\\n\\n![](./img/crawlfrontier.png)\\n\\nThere are two other developments that might be worth sharing since they affect how we provide access to data.  One is the growth of third-party scraping of OS Analytics -- both the free version and attempts to scrape via spam trial accounts.  As a scraping-based archival project, we have a lot sympathy for scraping as a baseline feature of the open web. But we are seeing very significant growth in scraping that, in the past year, has come close on several instances to bringing down OS Analytics. This is not a problem specific to Open Syllabus.  A recent Charleston conference panel of open data service providers put the percentage of traffic from bots to their sites at 86%. Waves of AI bot scrapers produce a drain on engineering resources and, of course, our sustainability model around the dataset.  And it\'s why, since March, if you visit Analytcs, you\'ll experience one or more Cloudflare prompts that try to separate the humans from the bots. I see no reason to think that this will stop, and some reason to fear that it will force open services further behind authentication walls.  \\n\\n![](./img/scrape.png)\\n\\nYes, that red line at the bottom is our normal traffic compared to recent (China s
1ourced) scraping efforts.\\n\\nThe other development is the growth in the number of data requests, mostly from researchers, that come into Open Syllabus. What in past years was a more-or-less once a month pace has increased to several per week and sometimes more.  Since each of these leads to a conversation, written agreement review by counsel, involvement of engineers, usually a payment, and often follow up, the higher numbers have been hard to manage for our small team. If you\'ve experienced this personally in the form of slow follow ups or deferred answers, I apologize.  Now you know why. The answer -- at least a partial answer -- will be coming shortly in the form of a large open \\"CC-NC\\" data release. More on that to come.\\n\\nIn the meantime, we\'re launching our Spring 2026 Account Jubilee, which will give all existing (expired) trial accounts renewed access through June 8.  If you\'ve been missing your full account access, enjoy."},{"id":"OER-Update-for-2024","metadata":{"permalink":"/OER-Update-for-2024","source":"@site/blog/2025-06-02-OER-update-2024.md","title":"OER Marches On in 2024","description":"With the release of the 2.13 dataset, we can update our analysis of OER adoption for 2024.  You can see the ealier 2023 update here and here for 2022.  And as always you can explore all of this in Analytics with a trial account or subscription.","date":"2025-06-02T00:00:00.000Z","formattedDate":"June 2, 2025","tags":[{"label":"opensyllabus","permalink":"/tags/opensyllabus"}],"readingTime":5.76,"hasTruncateMarker":false,"authors":[{"name":"Joe Karaganis","url":"https://github.com/karaganis","imageURL":"https://avatars.githubusercontent.com/u/5721537?v=4","key":"joe"}],"frontMatter":{"slug":"OER-Update-for-2024","title":"OER Marches On in 2024","authors":["joe"],"tags":["opensyllabus"]},"prevItem":{"title":"Spring 2026 Update","permalink":"/Spring-2026-Update"},"nextItem":{"title":"Spring 2025 News","permalink":"/spring-2025-news"}},"content":"![](./img/USA-AllSchools.png)\\n\\nWith the release of the 2.13 dataset, we can update our analysis of OER adoption for 2024.  You can see [the ealier 2023 update here](https://blog.opensyllabus.org/oer-adoption-update-2023) and [here for 2022](https://blog.opensyllabus.org/closing-the-oer-information-loop-2022-data).  And as always you can explore all of this in [Analytics](https://analytics.opensyllabus.org) with a trial account or subscription.  \\n\\nTo revisit some of the earlier context:\\n\\n> A few years ago we reported on the rates of growth in the adoption of \u2018Open Educational Resource\u2019 (OER) textbooks and \u2018Open Access\u2019 (OA) monographs. These are books published under Creative Commons licenses, which means that they can be used and circulated freely. In a world of $200 commercial textbooks, OER textbooks, in particular, have become an important part of school and state efforts to reduce student costs.\\n> But free has a few drawbacks. In markets for commercial textbooks (and most other goods), supply and demand are connected by the sale. Producers and consumers communicate through this information loop, and this relationship makes the market more or less efficient and \u2014 on the supply side \u2014 capable of adjusting.\\n> The information loop for free digital books, on the other hand, isn\u2019t closed. There is no sale or single point of access and titles are copied and circulate freely. It\u2019s hard, accordingly, to know what the demand side of the OER and OA ecosystems looks like. And this lack of information becomes a problem for authors and publishers (the producers) and faculty and students (the consumers). Decisions to invest time and money by faculty, funders, libraries and others in creating new titles are made without strong insight into the demand for existing ones. Adoption decisions by faculty and staff are made without much visibility into the experience of other programs, which could provide models. Both sides of the equation involve risks, that those risks are hard to mitigate.\\n> Open Syllabus can construct this demand side information from syllabi, and so partially close the information loop. \\n\\nSo what\'s new? \\n\\n[US data](https://analytics-dev.opensyllabus.org/oer-metrics?oer_query=oer-chart&size=10&country_name=United+States&institution_country_codes=US) shows continuing growth of over 20% annually, with open titles now assigned in over 2% of all classes.  The overall story is still one of fast growth but from a near-zero baseline in the mid 2010s -- and with a lot of variation by field and school type.\\n\\nUS adoption is primarily about textbook adoption \u2014 especially into the general education curriculum and [especially at two-year colleges](https://analytics-dev.opensyllabus.org/oer-metrics?oer_query=oer-chart&size=10&country_name=United+States&institution_country_codes=US&institution_types=two_year), which have been the focus of a number of state-level adoption efforts.  Rates of OA monograph adoption, in contrast, have remained nearly unchanged over the peri
1od.  We would attribute this to the much greater opportunities to scale up adoption of introductory textbooks compared to specialized scholarly work \u2014 though this pattern does not hold up consistently in other countries.\\n\\n![](./img/USA2Year.png)\\n\\nThe fastest growth that we can measure is still in the [California 2-year system](https://analytics-dev.opensyllabus.org/oer-metrics?oer_query=oer-chart&size=10&country_name=United+States&institution_country_codes=US&institution_types=two_year&institution_state_codes=US-CA&state_query=California), which benefits from the largest state-supported adoption program in the country.  Nearly 6% of Calfornia 2-Year college classes use OER textbooks.\\n\\n![](./img/California2Year.png)\\n\\nThere are still major differences by field, reflecting the still-very-uneven availability of open titles in the gen ed curriculum.  Math and Computer Science remain at the top of the list.  At California two-year schools, [over 20% of math courses use open titles](https://analytics-dev.opensyllabus.org/oer-metrics?oer_query=oer-chart&size=10&country_name=United+States&institution_country_codes=US&institution_types=two_year&institution_state_codes=US-CA&state_query=California&field_name=Mathematics&field_ids=45). \\n\\n![](./img/Cal2YearMath.png)\\n\\nTake off can be rapid when a title successfully breaks into a major gen ed category, such as [American Government survey classes](https://analytics-dev.opensyllabus.org/oer-metrics?oer_query=oer-chart&size=10&country_name=United+States&institution_country_codes=US&institution_types=two_year&field_name=Political+Science&field_ids=56).  Glenn Krutz\u2019 American Government textbook (2021) is responsible for almost all of the growth below \u2014 and in fact is higher than the chart indicates because syllabi routinely misspell his name as Kurtz, which leads to a citation undercount.\\n\\n![](./img/US2YearPolSci.png)\\n\\nMuch the same is true in [Psychology](https://analytics-dev.opensyllabus.org/oer-metrics?oer_query=oer-chart&size=10&country_name=United+States&institution_country_codes=US&institution_types=two_year&field_name=Psychology&field_ids=57), where [Spielman\u2019s Psychology](https://analytics-dev.opensyllabus.org/oer-metrics?oer_query=titles&size=10&country_name=United+States&institution_country_codes=US&institution_types=two_year&field_name=Psychology&field_ids=57) (2020) has broken into the Intro to Psychology text pool.\\n\\n![](./img/US2YearPsych.png)\\n\\nOther fields are comparatively underserved, with no breakthrough titles.  History, Sociology, Business, and Engineering are examples.\\n\\nInternational data continues to show interesting variation.  Canadian adoption patterns look a lot like the US, with a focus on introductory textbooks.\\n\\n![](./img/Canada.png)\\n\\nThe [UK, in contrast, has high levels of OA monograph adoption](https://analytics-dev.opensyllabus.org/oer-metrics?oer_query=oer-chart&size=10&country_name=United+Kingdom&institution_country_codes=GB), but very low levels for textbooks.  The ease of construction of secondary bibliographies in widely-used reading list tools like Talis and Leganto probably boosts the monograph numbers, but textbook adoption has clearly not been a focus.\\n\\n![](./img/UK.png)\\n\\nOther non-reading-list-using European countries show a similar pattern: heavy on monographs but light on textbooks.  In earlier updates, we attributed this to the ease with which English-language research monographs circulate as optional reading through international research communities compared to the work of producing language-localized versions of open textbooks.  This still seems plausible to us.  [Italy is typical in this regard.](https://analytics-dev.opensyllabus.org/oer-metrics?oer_query=oer-chart&size=10&country_name=Italy&institution_country_codes=IT).\\n\\n![](./img/Italy.png)\\n\\n[Australia](https://analytics-dev.opensyllabus.org/oer-metrics?oer_query=oer-chart&size=10&country_name=Australia&institution_country_codes=AU) falls between the two models.\\n\\n![](./img/Australia.png)\\n\\nAs we put it last year:\\n> The data outside the US and Canada point to differences between OER textbook publishing and \'OA\' or open access scholarly monograph publishing, which remains the province of university presses. For OER textbook authors and publishers, the goal is to provide substitutes for commercial titles used in popular classes. An OER textbook can have a steep adoption curve because it has a large potential pool of classes to convert. This conversion process, in turn, has been formalized into policy and advocacy approaches, and has become an official responsibility of the library at many schools.\\n\\n> OA monographs, in contrast, come out of a more-or-less parallel but distinct movement to ensure that published research is free -- beginning with journal articles but extending to books and other research outputs. As research, OA monographs are usually specialized titles and almost by definition not intended to be direct substitutes for existing (commercial) titles.\\n\\n> The incentives and growth potential are accordingly different. Publishers are publishing more OA titles (measured by growth of the Directory of Open Access Books ), but they tend to fill small course niches and show up in classes that already assign a lot of other (commercial) books. The US and Canadian advocacy model doesn\'t apply as easily in this context, \'free\' has less of an impact, and the textbook-based curriculum of two-year schools is largely irrelevant. This is, we think, the context for what we see in the US and Canadian data, which shows modest growth at best in the adoption of OA titles over the past ten years.\\n\\n> But elsewhere, the monograph vs textbook data is different and I don\'t think we have a complete explanation of it. One important factor is that the market-size dynamic is reversed: it\'s entry-level textbooks that are fragmented by country and language -- and dominated by translated commercial textbook titles -- while the research culture is international and built around English. English-language research monographs \'travel\' better in this context and cost/benefit decisions shift accordingly. \\n\\nWe\u2019ll refer back to [last year\u2019s update for a discussion of some of the caveats around the data](https://blog.opensyllabus.org/oer-adoption-update-2023) \u2014 including the likelihood that OS undercounts OER adoptions, but probably not by a lot.  You can learn more about OS\'s methods there too."},{"id":"spring-2025-news","metadata":{"permalink":"/spring-2025-news","
1source":"@site/blog/2025-05-29-spring-2025-news.md","title":"Spring 2025 News","description":"We have a lot!","date":"2025-05-29T00:00:00.000Z","formattedDate":"May 29, 2025","tags":[{"label":"opensyllabus","permalink":"/tags/opensyllabus"}],"readingTime":1.715,"hasTruncateMarker":false,"authors":[{"name":"Joe Karaganis","url":"https://github.com/karaganis","imageURL":"https://avatars.githubusercontent.com/u/5721537?v=4","key":"joe"}],"frontMatter":{"slug":"spring-2025-news","title":"Spring 2025 News","authors":["joe"],"tags":["opensyllabus"]},"prevItem":{"title":"OER Marches On in 2024","permalink":"/OER-Update-for-2024"},"nextItem":{"title":"The OS Analytics Top 100 Books of the 21st Century","permalink":"/top-100-assigned"}},"content":"We have a lot!  \\n\\nFirst, we\u2019re very pleased to announce a strategic partnership with [VitalSource](https://www.vitalsource.com/), the world\u2019s leading learning delivery network.  This represents is a big vote of confidence in our work and provides a path to improving OS services in a variety of ways in the years to come. It\u2019s also a significant step for VitalSource into the world of open data and open educational resources, which have been at the core of Open Syllabus\u2019s work.  \\n\\nWhat does strategic partnership mean?  In short, it means that VitalSource will provide financial and technical support to Open Syllabus and become the primary commercial path for accessing premium versions of OS services like Analytics.  Open Syllabus remains an independent nonprofit organization and will continue to pursue its mission of supporting curricular research, archiving curricular data, and building teaching and learning services.   \\n\\nWhat changes for regular users?  Nothing.  Analytics, the Galaxy, and the Course Matcher will continue to provide access to most data and services for free.   \\n\\nWhat changes for subscribing institutions?  Open Syllabus services become better \u2013 drawing on resources that go beyond those of what has been, over the past five years, a four-person team. \\n\\nWith VitalSource\u2019s support, Open Syllabus can continue to grow into what we\u2019ve wanted it to be: the global archive for curricular information and core infrastructure for higher education.   \\n\\nSecond, we\u2019ve released a new version of the [Analytics](https://analytics.opensyllabus.org) dataset. \\n\\nThe new dataset (Version 2.13 for those counting) adds six million new syllabi, bringing the total to 27.6 million and representing the single largest increase in OS history.  It updates and expands the range of discoverable titles and incorporates a range of improvements to data quality.  \\n\\nThird, we will be releasing a research dataset with seven and a half million syllabi representing eight years of the OS syllabus collection (2010-2017).  This dataset will be available to academic researchers and nonprofit organizations -- with more information to come.\\n\\nAnd there is more in the pipeline for the next months. It\'s an exciting time for Open Syllabus and we hope you\'ll stay tuned."},{"id":"top-100-assigned","metadata":{"permalink":"/top-100-assigned","source":"@site/blog/2024-07-13-top-100-assigned.md","title":"The OS Analytics Top 100 Books of the 21st Century","description":"The New York Times recently published a list of the \'100 Best\' books of the 21st Century, drawing on recommendations from authors and critics -- and it is generally excellent.  And also completely subjective in the way all best-of lists are, and that\'s part of the fun.","date":"2024-07-13T00:00:00.000Z","formattedDate":"July 13, 2024","tags":[{"label":"opensyllabus","permalink":"/tags/opensyllabus"}],"readingTime":8.705,"hasTruncateMarker":false,"authors":[{"name":"Joe Karaganis","url":"https://github.com/karaganis","imageURL":"https://avatars.githubusercontent.com/u/5721537?v=4","key":"joe"}],"frontMatter":{"slug":"top-100-assigned","title":"The OS Analytics Top 100 Books of the 21st Century","authors":["joe"],"tags":["opensyllabus"]},"prevItem":{"title":"Spring 2025 News","permalink":"/spring-2025-news"},"nextItem":{"title":"Galaxy Brain","permalink":"/Galaxy-Brain"}},"content":"The New York Times recently published [a list of the \'100 Best\' books of the 21st Century](https://www.nytimes.c
1om/interactive/2024/books/best-books-21st-century.html), drawing on recommendations from authors and critics -- and it is generally excellent.  And also completely subjective in the way all best-of lists are, and that\'s part of the fun.\\n\\nIt got me wondering what the analogous \'100 Most Frequently Taught\' books of the 21st century would look like if one removed the  primarily \'instructional\' genres that crowd the top ranks in Open Syllabus Analytics -- the textbooks, reference guides, readers, introductions to X, and similar titles. So that\'s what I\'ve tried to do here.\\n\\nLet\'s start by noting that \'Most Frequently Taught\' does not mean \'Best,\' nor is it [a stamp of approval of the author or the ideas](https://blog.opensyllabus.org/so-you-want-to-write-tweet-something-about-the-the-communist-manifestos-high-rank-in-the-os-dataset). A title can serve multiple pedagogical purposes, including exposure to work that is, for better or for worse, \'influential.\' But I think it\'s fairly easy to see when the selection of a *contemporary* title, especially, does appear to reflect a judgement about what\'s best to illustrate an argument, introduce a perspective, or illuminate a topic.  \\n\\nYou have to go pretty far down the title ranks in Analytics to identify 100 titles that (1) aren\'t in those pedagogical categories above; and (2) were published after 1999. 2600 titles specifically.  And you learn a few things when you do.\\n\\nFirst, the corpus of teaching titles is old.  There are very few widely-used titles published in this century.  The approach for most instructional materials is to continuously update titles published -- often -- decades ago.  And the most successful textbooks are no longer books so much as brands or product lines. Pedagogically, [higher ed is very conservative](https://blog.opensyllabus.org/the-conservative-university).  \\n\\nSecond, the results cluster around a few topics: globalization and economic development, leadership and management, cognitive science, new media, and science writing are the largest categories.  I think that reflects three conditions for appearing in the top ranks: large fields do better than small fields -- e.g., business, economics, psychology, and political science over religion or anthropology or classics; fields that assign books (rather than textbooks) to undergrads do better than fields that don\'t, so very little STEM presence; and fields that track and develop new ideas around contemporary events have a stronger appetite for new titles. It would be possible to continue down the ranks and identify, for example, the top 100 assigned contemporary novels but in practice the literature side of English is a fairly small and mostly historical field and [contemporary fiction is a niche within it](https://blog.opensyllabus.org/about-that-nobel-prize).  Only a few literary titles appear near the top. \\n\\nSo this is not a literary best list, but arguably best for learning something important about the topics that have clustered at the top of the ranks, and best for understanding books that have become influential in thinking about those topics.  The overlap with the NYT list is small, but does feature a few rapidly canonized literary titles. I\'ve indicated the overlapping titles with a smiley.  And since I was enjoying this excercise I extended the ranking a bit to 116.  So you get more best books from Open Syllabus than from the NYT.\\n\\nThe links go to title and author profile pages in [Open Syllabus Analytics](https://analytics.opensyllabus.org). You can sign up for free to explore the full data. \\n\\n\\n\\n1. [Leading Change](https://analytics.opensyllabus.org/singleton/works?id=670014954455), [John P. Kotter](https://analytics.opensyllabus.org/singleton/authors?id=John%20P.%20Kotter)\\n2. [Thinking, Fast and Slow](https://analytics.opensyllabus.org/singleton/works?id=816043916583), [Daniel Kahneman](https://analytics.opensyllabus.org/singleton/authors?id=Daniel%20Kahneman)\\n3. [Bowling Alone: The Collapse and Revival of American Community](https://analytics.opensyllabus.org/singleton/works?id=1047972109180), [Robert D. Putnam](https://analytics.opensyllabus.org/singleton/authors?id=Robert%20D.%20Putnam)\\n4. [Development as Freedom](https://analytics.opensyllabus.org/singleton/works?id=575525788291), [Amartya Sen](https://analytics.opensyllabus.org/singleton/authors?id=Amartya%20Sen)\\n5. [Convergence Culture: Where Old and New Media Collide](https://analytics.opensyllabus.org/singleton/works?id=2886218215356), [Henry Jenkins](https://analytics.opensyllabus.org/singleton/authors?id=Henry%20Jenkins)\\n6. :grinning: [The New Jim Crow](https://analytics.opensyllabus.org/singleton/works?id=549755826590), [Michelle Alexander](https://analytics.opensyllabus.org/singleton/authors?id=Michelle%20Alexander)\\n7. [The Language of New Media](https://analytics.opensyllabus.org/singleton/works?id=111669276880), [Lev Manovich](https://analytics.opensyllabus.org/singleton/authors?id=Lev%20Manovich)\\n8. [Globalization and Its Discontents](https://analytics.opensyllabus.org/singleton/works?id=3942780063299), [Joseph E. Stiglitz](https://analytics.opensyllabus.org/singleton/authors?id=Joseph%20E.%20Stiglitz)\\n9. [The Spirit Level: Why Greater Equality Makes Societies Stronger](https://analytics.opensyllabus.org/singleton/works?id=386547205233), [Richard G. Wilkinson](https://analytics.opensyllabus.org/singleton/authors?id=Richard%20G.%20Wilkinson)\\n10. [Good to Great](https://analytics.opensyllabus.org/singleton/works?id=2052994454549), [Jim Collins](https://analytics.opensyllabus.org/singleton/authors?id=Jim%20Collins)\\n11. :grinning: [Persepolis](https://analytics.opensyllabus.org/singleton/works?id=3186865840131), [Marjane Satrapi](https://analytics.opensyllabus.org/singleton/authors?id=Marjane%20Satrapi)\\n12. [A Brief History of Neoliberalism](https://analytics.opensyllabus.org/singleton/works?id=283467882842), [David Harvey](https://analytics.opensyllabus.org/singleton/authors?id=David%20Harvey)\\n13. [Courageous Faith: Life Lessons From Old Testament Heroes](https://analytics.opensyllabus.org/singleton/works?id=8710193849277), [Edward E. Hindson](https://analytics.opensyllabus.org/singleton/authors?id=Edward%20E.%20Hindson)\\n14. [Blue Ocean Strategy: How to Create Uncontested Market Space and Make the Competition Irrelevant](https://analytics.opensyllabus.org/singleton/works?id=1073741981800), [W. Chan Kim,  Renee Mauborgne](https://analytics.opensyllabus.org/singleton/authors?id=W.%20Chan%20Kim)\\n15. [The Lean Startup](https://analytics.opensyllabus.org/singleton/works?id=1915555608444), [Eric Ries](https://analytics.opensyllabus.org/singleton/authors?id=Eric%20Ries)\\n16. :grinning: [Nickel and Dimed: On (Not) Getting by in America](https://analytics.opensyllabus.org/singleton/works?id=721554599077), [Barbara 
1Ehrenreich](https://analytics.opensyllabus.org/singleton/authors?id=Barbara%20Ehrenreich)\\n17. [The World Is Flat: A Brief History of the Twenty-First Century](https://analytics.opensyllabus.org/singleton/works?id=2422361725885), [Thomas L. Friedman](https://analytics.opensyllabus.org/singleton/authors?id=Thomas%20L.%20Friedman)\\n18. [Influence](https://analytics.opensyllabus.org/singleton/works?id=68719626561), [Robert B. Cialdini](https://analytics.opensyllabus.org/singleton/authors?id=Robert%20B.%20Cialdini)\\n19. [The Tipping Point: How Little Things Can Make a Big Difference](https://analytics.opensyllabus.org/singleton/works?id=85899504662), [Malcolm Gladwell](https://analytics.opensyllabus.org/singleton/authors?id=Malcolm%20Gladwell)\\n20. [Eats, Shoots and Leaves](https://analytics.opensyllabus.org/singleton/works?id=2250562916558), [Lynne Truss](https://analytics.opensyllabus.org/singleton/authors?id=Lynne%20Truss)\\n21. :grinning: [Fun Home: A Family Tragicomic](https://analytics.opensyllabus.org/singleton/works?id=326417586386), [Alison Bechdel](https://analytics.opensyllabus.org/singleton/authors?id=Alison%20Bechdel)\\n22. [On Writing: A Memoir of the Craft](https://analytics.opensyllabus.org/singleton/works?id=1254130546723), [Stephen King](https://analytics.opensyllabus.org/singleton/authors?id=Stephen%20King)\\n23. [The Tragedy of Great Power Politics](https://analytics.opensyllabus.org/singleton/works?id=8710194031409), [John J. Mearsheimer](https://analytics.opensyllabus.org/singleton/authors?id=John%20J.%20Mearsheimer)\\n24. [The End of Poverty: Economic Possibilities for Our Time](https://analytics.opensyllabus.org/singleton/works?id=2800318781461), [Jeffrey D. Sachs](https://analytics.opensyllabus.org/singleton/authors?id=Jeffrey%20D.%20Sachs)\\n25. [Outliers: The Story of Success](https://analytics.opensyllabus.org/singleton/works?id=6966437088472), [Malcolm Gladwell](https://analytics.opensyllabus.org/singleton/authors?id=Malcolm%20Gladwell)\\n26. [The Rise of the Creative Class](https://analytics.opensyllabus.org/singleton/works?id=1803886279729), [Richard L. Florida](https://analytics.opensyllabus.org/singleton/authors?id=Richard%20L.%20Florida)\\n27. [Modernity at Large: Cultural Dimensions of Globalization](https://analytics.opensyllabus.org/singleton/works?id=6150393176690), [Arjun Appadurai](https://analytics.opensyllabus.org/singleton/authors?id=Arjun%20Appadurai)\\n28. [Collapse: How Societies Choose to Fail or Succeed](https://analytics.opensyllabus.org/singleton/works?id=2078764182318), [Jared Diamond](https://analytics.opensyllabus.org/singleton/authors?id=Jared%20Diamond)\\n29. [The Five Dysfunctions of a Team: A Leadership Fable](https://analytics.opensyllabus.org/singleton/works?id=4312147249679), [Patrick Lencioni](https://analytics.opensyllabus.org/singleton/authors?id=Patrick%20Lencioni)\\n30. [The Immortal Life of Henrietta Lacks](https://analytics.opensyllabus.org/singleton/works?id=8658654349885), [Rebecca Skloot](https://analytics.opensyllabus.org/singleton/authors?id=Rebecca%20Skloot)\\n31. [Capital in the Twenty-First Century](https://analytics.opensyllabus.org/singleton/works?id=438086675179), [Thomas Piketty](https://analytics.opensyllabus.org/singleton/authors?id=Thomas%20Piketty)\\n32. [Poor Economics: A Radical Rethinking of the Way to Fight Global Poverty](https://analytics.opensyllabus.org/singleton/works?id=2491081042732), [Abhijit Banerjee, Esther Duflo](https://analytics.opensyllabus.org/singleton/authors?id=Abhijit%20Banerjee)\\n33. [Why Nations Fail: The Origins of Power, Prosperity and Poverty](https://analytics.opensyllabus.org/singleton/works?id=3959960003050), [Daron Acemo\u011flu, James A. Robinson](https://analytics.opensyllabus.org/singleton/authors?id=Daron%20Acemo%C4%9Flu)\\n34. [Globalization](https://analytics.opensyllabus.org/singleton/works?id=154618952260), [Manfred B. Steger](https://analytics.opensyllabus.org/singleton/authors?id=Manfred%20B.%20Steger)\\n35. [Fast Food Nation: The Dark Side of the All-American Meal](https://analytics.opensyllabus.org/singleton/works?id=3908420367646), [Eric Schlosser](https://analytics.opensyllabus.org/singleton/authors?id=Eric%20Schlosser)\\n36. :grinning: [Between the World and Me](https://analytics.opensyllabus.org/singleton/works?id=8589934928830), [Ta-Nehisi Coates](https://analytics.opensyllabus.org/singleton/authors?id=Ta-Nehisi%20Coates)\\n37. :grinning: [Postwar: A History of Europe Since 1945](https://analytics.opensyllabus.org/singleton/works?id=8598524642786), [Tony Judt](https://analytics.opensyllabus.org/singleton/authors?id=Tony%20Judt)\\n38. [Every Good Endeavor: Connecting Your Work to God\'s Work](https://analytics.opensyllabus.org/singleton/works?id=5841155696206), [Timothy J. Keller](https://analytics.opensyllabus.org/singleton/authors?id=Timothy%20J.%20Keller)\\n39. [Alone Together: Why We Expect More From Technology and Less From Each Other](https://analytics.opensyllabus.org/singleton/works?id=764504187457), [Sherry Turkle](https://analytics.opensyllabus.org/singleton/authors?id=Sherry%20Turkle)\\n40. [Empire](https://analytics.opensyllabus.org/singleton/works?id=1975685046465), [Michael Hardt, Toni Negri](https://analytics.opensyllabus.org/singleton/authors?id=Michael%20Hardt)\\n41. [Justice](https://analytics.opensyllabus.org/singleton/works?id=2130303926498), [Michael J. Sandel](https://analytics.opensyllabus.org/singleton/authors?id=Michael%20J.%20Sandel)\\n42. [Postcolonialism: An Historical Introduction](https://analytics.opensyllabus.org/singleton/works?id=481036379423), [Robert Young](https://analytics.opensyllabus.org/singleton/authors?id=Robert%20Young)\\n43. [Change by Design: How Design Thinking Transforms Organizations and Inspires Innovation](https://analytics.opensyllabus.org/singleton/works?id=1864016002155), [Tim Brown](https://analytics.opensyllabus.org/singleton/authors?id=Tim%20Brown)\\n44. [The Art of Game Design: A Book of Lenses](https://analytics.opensyllabus.org/singleton/works?id=1580547988138), [Jesse Schell](https://analytics.opensyllabus.org/singleton/authors?id=Jesse%20Schell)\\n45. [Bird by Bird: Some Instructions on Writing and Life](https://analytics.opensyllabus.org/singleton/works?id=867583396086), [Anne Lamott](https://analytics.opensyllabus.org/singleton/authors?id=Anne%20Lamott)\\n46. [Predictably Irrational: The Hidden Forces That Shape Our Decisions](https://analytics.opensyllabus.org/singleton/works?id=858993511701), [Dan Ariely](https://analytics.opensyllabus.org/singleton/authors?id=Dan%20Ariely)\\n47. [The Culture of Control](https://analytics.opensyllabus.org/singleton/works?id=884763264872), [David Garland](https://analytics.opensyllabus.org/singleton/authors?id=David%20Garland)\\n48. [Freakonomics: A Rogue Economist Explores the Hidden Side of Everything](https://analytics.opensyllabus.org/singleton/works?id=8753143506491), [Steven D. Levitt](https://analytics.opensyllabus.org/singleton/authors?id=Steven%20D.%20Levitt)\\n49. [A History of the Modern Middle East](https://analytics.opensyllabus.org/singleton/works?id=137438960591), [William L. Cleveland](https://analytics.opensyllabus.org/singleton/authors?id=William%20L.%20Cleveland)\\n50. [Strategy: An International Perspective](https://analytics.opensyllabus.org/singleton/works?id=2671469701459), [Bob De Wit](https://analytics.opensyllabus.org/singleton/authors?id=Bob%20De%20Wit)\\n51. [The Omnivore\'s Dilemma: A Natural History of Four Meals](https://analytics.opensyllabus.org/singleton/works?id=3195455677567), [Michael Pollan](https://analytics.opensyllabus.org/singleton/authors?id=Michael%20Pollan)\\n52. [Planet of Slums](https://analytics.opensyllabus.org/singleton/works?id=541165902552), [Mike Davis](https://analytics.opensyllabus.org/singleton/authors?id=Mike%20Davis)\\n53. [Reassembling the Social](https://analytics.opensyllabus.org/singleton/works?id=7344394162937), [Bruno Latour](https://analytics.opensyllabus.org/singleton/authors?id=Bruno%20Latour)\\n54. [Provincializing Europe: Postc
1olonial Thought and Historical Difference](https://analytics.opensyllabus.org/singleton/works?id=2001454760213), [Dipesh Chakrabarty](https://analytics.opensyllabus.org/singleton/authors?id=Dipesh%20Chakrabarty)\\n55. [The Wealth of Networks: How Social Production Transforms Markets and Freedom](https://analytics.opensyllabus.org/singleton/works?id=1116691676492), [Yochai Benkler](https://analytics.opensyllabus.org/singleton/authors?id=Yochai%20Benkler)\\n56. :grinning: [Citizen: An American Lyric](https://analytics.opensyllabus.org/singleton/works?id=6287832241557), [Claudia Rankine](https://analytics.opensyllabus.org/singleton/authors?id=Claudia%20Rankine)\\n57. [The Curious Incident of the Dog in the Night-Time](https://analytics.opensyllabus.org/singleton/works?id=695784774724), [Mark Haddon](https://analytics.opensyllabus.org/singleton/authors?id=Mark%20Haddon)\\n58. [This Changes Everything: Capitalism vs. The Climate](https://analytics.opensyllabus.org/singleton/works?id=6296422199594), [Naomi Klein](https://analytics.opensyllabus.org/singleton/authors?id=Naomi%20Klein)\\n59. [Emotional Design: Why We Love (Or Hate) Everyday Things](https://analytics.opensyllabus.org/singleton/works?id=1503238592661), [Donald A. Norman](https://analytics.opensyllabus.org/singleton/authors?id=Donald%20A.%20Norman)\\n60. :grinning: [Never Let Me Go](https://analytics.opensyllabus.org/singleton/works?id=8693013922332), [Kazuo Ishiguro](https://analytics.opensyllabus.org/singleton/authors?id=Kazuo%20Ishiguro)\\n61. [Free Culture](https://analytics.opensyllabus.org/singleton/works?id=3358664608206), [Lawrence Lessig](https://analytics.opensyllabus.org/singleton/authors?id=Lawrence%20Lessig)\\n62. :grinning: [The Road](https://analytics.opensyllabus.org/singleton/works?id=8598524565341), [Cormac McCarthy](https://analytics.opensyllabus.org/singleton/authors?id=Cormac%20McCarthy)\\n63. [The Hunger Games](https://analytics.opensyllabus.org/singleton/works?id=8538395090985), [Suzanne Collins](https://analytics.opensyllabus.org/singleton/authors?id=Suzanne%20Collins)\\n64. [The Absolutely True Diary of a Part-Time Indian](https://analytics.opensyllabus.org/singleton/works?id=8778913322818), [Sherman Alexie](https://analytics.opensyllabus.org/singleton/authors?id=Sherman%20Alexie)\\n64. :grinning: [White Teeth: A Novel](https://analytics.opensyllabus.org/singleton/works?id=5136780973610), [Zadie Smith](https://analytics.opensyllabus.org/singleton/authors?id=Zadie%20Smith)\\n65. [The Shock Doctrine: The Rise of Disaster Capitalism](https://analytics.opensyllabus.org/singleton/works?id=8701604097763), [Naomi Klein](https://analytics.opensyllabus.org/singleton/authors?id=Naomi%20Klein)\\n66. [Cities of Tomorrow](https://analytics.opensyllabus.org/singleton/works?id=523986010163), [Peter Hall](https://analytics.opensyllabus.org/singleton/authors?id=Peter%20Hall)\\n67. [The Body Keeps the Score: Brain, Mind, and Body in the Healing of Trauma](https://analytics.opensyllabus.org/singleton/works?id=4398046557456), [Bessel A. Van Der Kolk](https://analytics.opensyllabus.org/singleton/authors?id=Bessel%20A.%20Van%20Der%20Kolk)\\n69. [Undoing Gender](https://analytics.opensyllabus.org/singleton/works?id=1391569564721), [Judith Butler](https://analytics.opensyllabus.org/singleton/authors?id=Judith%20Butler)\\n70. :grinning: [The Brief Wondrous Life of Oscar Wao](https://analytics.opensyllabus.org/singleton/works?id=8761733601571), [Junot D\xedaz](https://analytics.opensyllabus.org/singleton/authors?id=Junot%20D.%C3%ADaz)\\n71. [The Globalization Paradox](https://analytics.opensyllabus.org/singleton/works?id=137439116077), [Dani Rodrik](https://analytics.opensyllabus.org/singleton/authors?id=Dani%20Rodrik)\\n72. :grinning:[The Emperor of All Maladies: A Biography of Cancer](https://analytics.opensyllabus.org/singleton/works?id=2010044819850), [Siddhartha Mukherjee](https://analytics.opensyllabus.org/singleton/authors?id=Siddhartha%20Mukherjee)\\n73. [Long Tail: Why the Future of Business Is Selling Less of More](https://analytics.opensyllabus.org/singleton/works?id=111669269001), [Chris Anderson](https://analytics.opensyllabus.org/singleton/authors?id=Chris%20Anderson)\\n74. [The Global Cold War: Third World Interventions and the Making of Our Times](https://analytics.opensyllabus.org/singleton/works?id=3161096012544), [Odd Arne Westad](https://analytics.opensyllabus.org/singleton/authors?id=Odd%20Arne%20Westad)\\n75. [Vibrant Matter: A Political Ecology of Things](https://analytics.opensyllabus.org/singleton/works?id=4440996256894), [Jane Bennett](https://analytics.opensyllabus.org/singleton/authors?id=Jane%20Bennett)\\n77. [The Education Debate](https://analytics.opensyllabus.org/singleton/works?id=1769526638907), [Stephen J. Ball](https://analytics.opensyllabus.org/singleton/authors?id=Stephen%20J.%20Ball)\\n79. [Cold War: A New History](https://analytics.opensyllabus.org/singleton/works?id=867583396255), [John Lewis Gaddis](https://analytics.opensyllabus.org/singleton/authors?id=John%20Lewis%20Gaddis)\\n80. [The Great Divergence: China, Europe, and the Making of the Modern World Economy](https://analytics.opensyllabus.org/singleton/works?id=635655169993), [Kenneth Pomeranz](https://analytics.opensyllabus.org/singleton/authors?id=Kenneth%20Pomeranz)\\n81. [Making Globalization Work](https://analytics.opensyllabus.org/singleton/works?id=8667244048771), [Joseph E. Stiglitz](https://analytics.opensyllabus.org/singleton/authors?id=Joseph%20E.%20Stiglitz)\\n82. [Precarious Life: The Powers of Mourning and Violence](https://analytics.opensyllabus.org/singleton/works?id=7206955176115), [Judith Butler](https://analytics.opensyllabus.org/singleton/authors?id=Judith%20Butler)\\n83. [Bad Science: Quacks, Hacks, and Big Pharma Flacks](https://analytics.opensyllabus.org/singleton/works?id=6889127548795), [Ben Goldacre](https://analytics.opensyllabus.org/singleton/authors?id=Ben%20Goldacre)\\n84. [Chavs: The Demonization of the Working Class](https://analytics.opensyllabus.org/singleton/works?id=4561255282670), [Owen Peter Jones](https://analytics.opensyllabus.org/singleton/authors?id=Owen%20Peter%20Jones)\\n85. [Why Love Matters: How Affection Shapes a Baby\'s Brain](https://analytics.opensyllabus.org/singleton/works?id=979252628880), [Sue Gerhardt](https://analytics.opensyllabus.org/singleton/authors?id=Sue%20Gerhardt)\\n86. [Born in Blood and Fire: A Concise History of Latin America](https://analytics.opensyllabus.org/singleton/works?id=1503238730890), [John Charles Chasteen](https://analytics.opensyllabus.org/singleton/authors?id=John%20Charles%20Chasteen)\\n87. [In Defense of Globalization](https://analytics.opensyllabus.org/singleton/works?id=1580548088664), [Jagdish N. Bhagwati](https://analytics.opensyllabus.org/singleton/authors?id=Jagdish%20N.%20Bhagwati)\\n88. [Prosperity Without Growth: Economics for a Finite Planet](https://analytics.opensyllabus.org/singleton/works?id=996432497590), [Tim Jackson](https://analytics.opensyllabus.org/singleton/authors?id=Tim%20Jackson)\\n89. [Kicking Away the Ladder: Development Strategy in Historical Perspective](https://analytics.opensyllabus.org/singleton/works?id=34359929913), [Ha\u2010Joon Chang](https://analytics.opensyllabus.org/singleton/authors?id=Ha%E2%80%90Joon%20Chang)\\n90. [Pathologies of Power](https://analytics.opensyllabus.org/singleton/works?id=249108114220), [Paul Farmer](https://analytics.opensyllabus.org/singleton/authors?id=Paul%20Farmer)\\n91. [The Gene: An Intimate History](https://analytics.opensyllabus.org/singleton/works?id=1176821161453), [Siddhartha Mukherjee](https://analytics.opensyllabus.org/singleton/authors?id=Siddhartha%20Mukherjee)\\n92. [Politics of Piety: The Islamic Revival and the Feminist Subject](https://analytics.opensyllabus.org/singleton/works?id=979252655668), [Saba Mahmood](https://analytics.opensyllabus.org/singleton/authors?id=Saba%20Mahmood)\\n93. [Impossible Subjects](https://analytics.opensyllabus.org/singleton/works?id=4569845377355), [Mae M. Ngai](https://analytics.opensyllabus.org/singleton/authors?id=Mae%20M.%20Ngai)\\n94. [Non-Places: Introduction to an Anthropology of Supermodernity](https://analytics.opensyllabus.org/singleton/works?id=1915555541436), [Marc Auge](https://analytics.opensyllabus.org/singleton/authors?id=Marc%20Auge)\\n95. [A Theory of the Firm: Governance, Residual Claims, and Organizational Forms](https://analytics.opensyllabus.org/singleton/works?id=627065389488), [Michael C. Jensen](https://analytics.opensyllabus.org/singleton/authors?id=Michael%20C.%20Jensen)\\n96. [Created Equal](https://analytics.opensyllabus.org/singleton/works?id=3143916252905), [Jacqueline Jones](https://analytics.opensyllabus.org/singleton/authors?id=Jacqueline%20Jones)\\n97. [The Precariat: The New Dangerous Class](https://analytics.opensyllabus.org/singleton/works?id=137438995249),[ Guy Standing](https://analytics.opensyllabus.org/singleton/authors?id=Guy%20Standing)\\n98. [The Kite Runner](https://analytics.opensyllabus.org/singleton/works?id=1735166996174),[ Khaled Hosseini](https://analytics.opensyllabus.org/singleton/authors?id=K
1haled%20Hosseini)\\n99. [What Money Can\'t Buy: The Moral Limits of Markets](https://analytics.opensyllabus.org/singleton/works?id=2439541518279),[ Michael J. Sandel](https://analytics.opensyllabus.org/singleton/authors?id=Michael%20J.%20Sandel)\\n100. [The Art of Looking Sideways](https://analytics.opensyllabus.org/singleton/works?id=7241314908100),[ Alan Fletcher](https://analytics.opensyllabus.org/singleton/authors?id=Alan%20Fletcher)\\n101. [The Birth of the Modern World, 1780-1914: Global Connections and Comparisons](https://analytics.opensyllabus.org/singleton/works?id=8624294613102),[ C. A. Bayly](https://analytics.opensyllabus.org/singleton/authors?id=C.%20A.%20Bayly)\\n102. [Global Political Economy: Understanding the International Economic Order](https://analytics.opensyllabus.org/singleton/works?id=51539746765),[ Robert Gilpin](https://analytics.opensyllabus.org/singleton/authors?id=Robert%20Gilpin)\\n103. [This Time Is Different: Eight Centuries of Financial Folly](https://analytics.opensyllabus.org/singleton/works?id=712964703881),[ Carmen Reinhart, Kenneth Rogoff](https://analytics.opensyllabus.org/singleton/authors?id=Carmen%20Reinh
1art)\\n104. [A Modern History of Japan: From Tokugawa Times to the Present](https://analytics.opensyllabus.org/singleton/works?id=180388828487),[ Andrew Gordon](https://analytics.opensyllabus.org/singleton/authors?id=Andrew%20Gordon)\\n105. [Global Governance and the New Wars: The Merging of Development and Security](https://analytics.opensyllabus.org/singleton/works?id=429496790929), [Mark Duffield](https://analytics.opensyllabus.org/singleton/authors?id=Mark%20Duffield)\\n106. [The Economics of Climate Change](https://analytics.opensyllabus.org/singleton/works?id=1520418470187),[ N. H. Stern](https://analytics.opensyllabus.org/singleton/authors?id=N.%20H.%20Stern)\\n107. [Drive: The Surprising Truth About What Motivates Us](https://analytics.opensyllabus.org/singleton/works?id=8796093042886), [Daniel H. Pink](https://analytics.opensyllabus.org/singleton/authors?id=Daniel%20H.%20Pink)\\n108. [Craftsman](https://analytics.opensyllabus.org/singleton/works?id=8770323462650),[ Richard Sennett](https://analytics.opensyllabus.org/singleton/authors?id=Richard%20Sennett)\\n109. [Postdramatic Theatre](https://analytics.opensyllabus.org/singleton/works?id=2147483818013), [Hans-Thies Lehmann](https://analytics.opensyllabus.org/singleton/authors?id=Hans-Thies%20Lehmann)\\n110. [The Stripping of the Altars: Traditional Religion in England, C.1400-C.1580](https://analytics.opensyllabus.org/singleton/works?id=429496806325), [Eamon Duffy](https://analytics.opensyllabus.org/singleton/authors?id=Eamon%20Duffy)\\n111. [Natural Capitalism: Creating the Next Industrial Revolution](https://analytics.opensyllabus.org/singleton/works?id=240518261249), [Paul Hawken, Amory B. Lovins, L. Hunter Lovins](https://analytics.opensyllabus.org/singleton/authors?id=Paul%20Hawken)\\n112. [Dude, You\'re a Fag: Masculinity and Sexuality in High School](https://analytics.opensyllabus.org/singleton/works?id=1116691614492), [C. J. Pascoe](https://analytics.opensyllabus.org/singleton/authors?id=C.%20J.%20Pascoe)\\n113. [Digital Art](https://analytics.opensyllabus.org/singleton/works?id=8624294655683), [Christiane Paul](https://analytics.opensyllabus.org/singleton/authors?id=Christiane%20Paul)\\n114. [Into the Woods](https://analytics.opensyllabus.org/singleton/works?id=4887672988520), [John Yorke](https://analytics.opensyllabus.org/singleton/authors?id=John%20Yorke)\\n115. [Networks of Outrage and Hope: Social Movements in the Internet Age](https://analytics.opensyllabus.org/singleton/works?id=446676599060), [Manuel Castells](https://analytics.opensyllabus.org/singleton/authors?id=Manuel%20Castells)\\n116. [The Politics of the Environment: Ideas, Activism, Policy](https://analytics.opensyllabus.org/singleton/works?id=335007631190), [Neil Carter](https://analytics.opensyllabus.org/singleton/authors?id=Neil%20Carter)\\n117. [Place](https://analytics.opensyllabus.org/singleton/works?id=3006477243494), [Tim Cresswell](https://analytics.opensyllabus.org/singleton/authors?id=Tim%20Cresswell)\\n118. [A Problem From Hell: America and the Age of Genocide](https://analytics.opensyllabus.org/singleton/works?id=6150393318108), [Samantha Power](https://analytics.opensyllabus.org/singleton/authors?id=Samantha%20Power)\\n     \\n*** Updated to remove a couple mistakenly included older books and articles"},{"id":"Galaxy-Brain","metadata":{"permalink":"/Galaxy-Brain","source":"@site/blog/2024-06-28-galaxy-brain.md","title":"Galaxy Brain","description":"The reception of the \u2018Co-Assignment Galaxy\u2019 has been a minor ongoing puzzle in our work.  The Galaxy is generally recognized as the coolest thing we\u2019ve built \u2013 a navigable, bird\u2019s eye view of all of anglophone higher education and a very novel way of looking at fields and subfields.  And it does attract a lot of oohs and aahs. What is doesn\u2019t attract is substantive conversation, which has surprised me because it provides a way of visualizing and characterizing everyone\u2019s field.  And people argue about those constantly.","date":"2024-06-28T00:00:00.000Z","formattedDate":"June 28, 2024","tags":[{"label":"opensyllabus","permalink":"/tags/opensyllabus"}],"readingTime":2.37,"hasTruncateMarker":false,"authors":[{"name":"Joe Karaganis","url":"https://github.com/karaganis","imageURL":"https://avatars.githubusercontent.com/u/5721537?v=4","key":"joe"}],"frontMatter":{"slug":"Galaxy-Brain","title":"Galaxy Brain","authors":["joe"],"tags":["opensyllabus"]},"prevItem":{"title":"The OS Analytics Top 100 Books of the 21st Century","permalink":"/top-100-assigned"},"nextItem":{"title":"2.11 Update","permalink":"/2.11-update"}},"content":"![](./img/Galaxy-Brain-4.jpg)\\n\\nThe reception of the \u2018Co-Assignment Galaxy\u2019 has been a minor ongoing puzzle in our work.  The Galaxy is generally recognized as the coolest thing we\u2019ve built \u2013 a navigable, bird\u2019s eye view of *all* of anglophone higher education and a very novel way of looking at fields and subfields.  And it does attract a lot of *oohs* and *aahs*. What is doesn\u2019t attract is substantive conversation, which has surprised me because it provides a way of visualizing and characterizing everyone\u2019s field.  And people argue about those constantly.\\n\\nSo I\u2019ve taken a stab at the question of why the dog didn\u2019t bark \u2013 why people are fascinated by the Galaxy but don\u2019t know what to do with it, or how to argue about it.  And the answer, I think, comes down mostly to the lack of habits of interpretation of large-scale plots, which are an increasingly common but also very recent way of representing data.  New forms of representation need new forms of literacy, and then the specifics of this representation \u2013 of fields composed of teaching choices \u2013 bring additional requirements.  I\u2019ve explored both issues [in a piece at Public Books](https://www.publicbooks.org/galaxy-brain/), and in the process tried to convey what I think we can learn about fields and the structure of higher ed from the Galaxy.  \\n\\n> Big, well-consolidated disciplines often resemble archipelagos that spread out along their major teaching subdivisions. This subfield geography in English is distinctive and reflects the continued periodization of teaching by century, country, and\u2014to a lesser extent\u2014genre.\\n\\n>
1 The disciplinary gravity that holds these clusters together, though, can be fragile. In the real world, classes that tick more than one disciplinary box can be cross listed. In the Galaxy this tension has to be resolved through color and spatial location. Most fields have inflection points where disciplinary commonalities can be outcompeted by strong thematic or problem-centered curricula, which \u201cpull\u201d titles into their orbits. Such tensions between disciplinary and topical organizations of teaching and research have been the starting point for many new fields over the years, such as the area studies fields that emerged in the postwar decades in part through SSRC programs.\\n\\n> Area studies fields turn out to be strong attractors of disciplinary titles. There is, for example, a large semicircular cluster for Asian and South Asian studies, composed of an arc running from India in the southeast to China and Japan in the northwest. What\u2019s the main thematic connector between South Asia and East Asia? The study of Hinduism and Buddhism, here in teal and gray. There is a corresponding regional Africa cluster with differentiated political science, history, and literature subclusters and a similar one for Latin America. And there is a more nebulous cluster covering the Middle East, North Africa, and the study of Islam\u2014with the last contributing a strong interdisciplinary dimension to the field."},{"id":"2.11-update","metadata":{"permalink":"/2.11-update","source":"@site/blog/2024-05-03-2.11-update.md","title":"2.11 Update","description":"We launched a major update to Open Syllabus a few weeks ago that includes a new version of the dataset, new features, and some changes to how you can explore Open Syllabus data.","date":"2024-05-03T00:00:00.000Z","formattedDate":"May 3, 2024","tags":[{"label":"opensyllabus","permalink":"/tags/opensyllabus"}],"readingTime":2.765,"hasTruncateMarker":false,"authors":[{"name":"Joe Karaganis","url":"https://github.com/karaganis","imageURL":"https://avatars.githubusercontent.com/u/5721537?v=4","key":"joe"}],"frontMatter":{"slug":"2.11-update","title":"2.11 Update","authors":["joe"],"tags":["opensyllabus"]},"prevItem":{"title":"Galaxy Brain","permalink":"/Galaxy-Brain"},"nextItem":{"title":"OER Adoption Update: 2023","permalink":"/oer-adoption-update-2023"}},"content":"We launched a major update to Open Syllabus a few weeks ago that includes a new version of the dataset, new features, and some changes to how you can explore Open Syllabus data.\\n\\n## Data\\nFirst, the dataset (v 2.11).  Our goal with every update is to increase both the size and quality of the dataset.  With the new version, we\u2019ve added about a million new syllabi from the US, 300,000 from the UK and Australia, and 150,000 from Canada.  The total number jumps from 18.7 million to 20.9 million.  The collection also has a bit more historical depth due to better digging through school archive sites \u2014 enough so that we\u2019re comfortable pushing back the starting year for charts to 2008 (from 2009).  We want to keep extending the historical window back, but this depends increasingly on faculty and school archival contributions.\\n\\nNext, data quality.  We have significantly expanded both the range of titles we can discover and the quality of our parsing of citations within syllabi.  First, the number of unique discovered titles in the collection has jumped from 3.4 million to 3.8 million.  This gain comes primarily from building a source catalog from ISBNs found within the collection, which has significantly expanded our view of non-US publishing in particular.  We also updated the citation catalog that we build from linked content in the dataset, which means that we have a better view of periodicals, websites, videos, and other non-traditional sources.  Here are the top-assigned titles, for example, from The Atlantic Monthly.\\n\\n![](./img/Atlantic.png)\\n\\nCombined with improvements in how we deal with the variety of citation practices used in syllabi, the number of citations discovered in the collection (across 21 million syllabi) has jumped from 56 million to 65 million.  In short, we\u2019re providing a more complete view of the same collection at the same time as we add to it.\\n\\n## Accounts\\nThe biggest change affects how you can explore the data.  We have shut down the old \u2018free\u2019 service, the Syllabus Explorer, in favor of creating an open tier for the new, better service, [Open Syllabus Analytics](https://analytics.opensyllabus.org).   Analytics now has open, trial, and subscription access.  The open tier is much better than the Syllabus Explorer, not least in the number of syllabi it includes, which jumps from 7.2 million to 11.9 million.  It retains a 2019 date cap (up from 2018) and, compared to the trial and subscription versions, more limited functionality.  You can sign up for a trial for free to explore these differences.  Our goal is to get schools to subscribe.\\n\\nAccounts have also changed. Now your account is just based on your email -- and if you sign up with a school email, it\'s immediately approved.  Logging in with an email keeps you logged in on that decice and browser.  \\n\\n![](./img/front.png)\\n\\n## The Gender Dashboard\\nWe\u2019ve also improved the Gender Data dashboard, which is available to logged in users.  Now you can search and filter across a wide range of dimensions, including country, field, and school.  Want to know, for example, what gender ratios look like across academic fields in [Australia](https://analytics.opensyllabus.org/gender?tab=fields&country_name=Australia&institution_country_codes=AU) or how those ratios [have changed in law in the US](https://analytics.opensyllabus.org/gender?tab=countries&country_name=United+States&institution_country_codes=US&field_name=Law&field_ids=40)?  Now you can.\\n\\n![](./img/AUgender.png)\\n\\nThat\u2019s it for 2.11!  Check it out \u2014 and if you like it, talk to your data services librarian about subscribing."},{"id":"oer-adoption-update-2023","metadata":{"permalink":"/oer-adoption-update-2023","source":"@site/blog/2024-04-09-OER-2023-update.md","title":"OER Adoption Update: 2023","description":"With the release of version 2.11 of the OS dataset, we can update our reporting on adoption rates for open educational resources (OERs) -- specifically, open textbooks and \'open access\' monographs.","date":"2024-04-09T00:00:00.000Z","formattedDate":"April 9, 2024","tags":[{"label":"opensyllabus","permalink":"/tags/opensyllabus"}],"readingTime":8.45,"hasTruncateMarker":false,"authors":[{"name":"Joe Karaganis","url":"https://github.com/karaganis","imageURL":"https://avatars.githubusercontent.com/u/5721537?v=4","key":"joe"}],"frontMatter":{"slug":"oer-adoption-update-2023","title":"OER Adoption Update: 2023","authors":["joe"],"tags":["opensyllabus"]},"prevItem":{"title":"2.11 Update","permalink":"/2.11-update"},"nextItem":{"title":"What Films Should We Teach","permalink":"/what-films-should-we-teach"}},"content":"![](./img/OERcitesUS.png)\\n\\nWith the release of version 2.11 of the OS dataset, we can update our reporting on adoption rates for open educational resources (OERs) -- specifically, open textbooks and \'open access\' monographs.\\n\\nThe defining thing about OERs is that they are released under an open license and so can be freely circulated and used. Because they are free, OERs have become an important part of efforts to lower the cost of higher education, which often includes thousands of dollars per student per year for course materials.  But free comes with a small catch: because there is no sale or single distribution point, it\'s difficult to track the use of OERs. And this difficulty makes it hard to know whether adoption efforts are working. And this lack of feedback complicates decisions about whether and where and how to invest in the open ecosystem.  The OER ecosystem is weaker, we would argue, because it\'s bad at closing this information loop between supply and demand.  \\n\\nAnd this is where Open Syllabus can play a role.  We track OER adoption via the appearance of titles on syllabi -- the point of use rather than the point of sale or distribution.  With nearly 21 million syllabi in the collection, we can provide accounts of adoption across individual schools, states, and countries, in and outside the US -- with some caveats that we\'ll get to below.\\n\\nThe new dataset adds two million syllabi and extends the timeline to 2023.  Broadly, it expands on the story we\'ve been telling for several years: rapid growth in OER textbook adoption in the US and Canada, but from a very low baseline in the mid 2010s, and rapid OER monograph adoption outside the US and Canada from similarly low baselines.  What does this mean concretely?\\n\\nIn 2013, OER textbooks were barely on the scene in the US and used in only around 1 in 400 classes.  By 2023, [it was 1 in 80](https://analytics.opensyllabus.org/oer-metrics?oer_query=oer-chart&size=10&country_name=United+States&institution_country_codes=US). Over the past 10 years, the growth rate in OER textbook use has averaged around 17.5% per year in the US. \\n\\nOER efforts have especially targeted two-year schools, where textbooks represent a larger portion of educational expenses and students are less able to afford them. This focus shows up clearly in the data.  The adoption rate at two years is [1 in 40 classes -- with a growth rate of 26% per year over the past 5 years](https://analytics.opensyllabus.org/oer-metrics?oer_query=oer-chart&size=10&country_name=United+States&institution_country_codes=US&institution_types=two_year).  Is this a tipping point?  It\'s beginning to look like one.\\n![](./img/OERcites2YRUS.png)\\n\\nBecause US higher ed 
1is very decentralized, OER adoption in the US is shaped by state, system, and school-level policies. California, which has the best-supported state-level program, remains the national leader by a large margin:  Over 1 in 40 classes use OER titles and 1 in 25 at two-year schools. \\n\\n![](./img/CaliOER.png)\\n\\nAmong the other states for which we have decent data: Texas and Michigan are close to average.  Georgia, Florida, Colorado, and Illinois fall below it.  \\n\\nWith respect to fields, math continues to be the leader, followed by computer science.  1 in 25 math classes use an OER textbook.  Restrict to 2-years and it\'s 1 in 18.  \\n\\nOther fields have begun to catch up as OER authors begin to fill some of the common entry-level class niches. Almost all of the growth in English and Education, for example, has happened since 2019 -- and in both cases due primarily to the emergence of [writing guides and student success manuals](https://analytics.opensyllabus.org/oer-metrics?oer_query=titles&size=10&field_name=English+Literature&field_ids=28&country_name=United+States&institution_country_codes=US). Methods books have gained traction across a number of social scientific and adjacent fields, including business.  And recently developed, topic-specific textbooks for core classes in [business](https://analytics.opensyllabus.org/oer-metrics?oer_query=titles&size=10&country_name=United+States&institution_country_codes=US&field_name=Business&field_ids=10), [psychology](https://analytics.opensyllabus.org/oer-metrics?oer_query=titles&size=10&country_name=United+States&institution_country_codes=US&field_name=Psychology&field_ids=57), and [political science](https://analytics.opensyllabus.org/oer-metrics?oer_query=titles&size=10&country_name=United+States&institution_country_codes=US&field_name=Political+Science&field_ids=56) have begun to gain traction.\\n\\n![](./img/EnglishOER.png)\\n\\nThe open book ecosystem in other countries offers interesting points of comparison. Canadian OER adoption looks very similar to the US, both in terms of the rate of growth and the heavy focus on textbooks.\\n\\n![](./img/CanadaOER.png)\\n\\nBut adoption elsewhere is more heavily tilted toward monographs.  [Adoption in the UK](https://analytics.opensyllabus.org/oer-metrics?oer_query=titles&size=10&country_name=United+Kingdom&institution_country_codes=GB) is the extreme case, with rapid monograph growth and a very small role for textbooks. \\n\\n![](./img/UKOER.png)\\n\\nThat downslope after 2020 reflects some data access problems related to a change in reading list software platforms in the UK (and Australia), so we are inclined to discount it. The more general pattern shows steady and in many cases very rapid monograph growth elsewhere in Europe where we have no such data problems. For example, Italy:\\n\\n![](./img/ItalyOER.png)\\n\\nAnd Portugal:\\n\\n![](./img/PortugalOER.png)\\n\\nAnd the Czech Republic:\\n\\n![](./img/CzechOER.png)\\n\\nAnd to an extent in the Netherlands:\\n\\n![](./img/NetherlandsOER.png)\\n\\nAnd also in Australia -- where overall adoption is lower but roughly evenly split between textbooks and monographs.\\n\\nThe data outside the US and Canada point to differences between OER textbook publishing and \'OA\' or open access scholarly monograph publishing, which remains the province of [university presses](https://analytics.opensyllabus.org/record/publishers?work_open_access=true).  For OER textbook authors and publishers, the goal is to provide substitutes for commercial titles used in popular classes.  An OER textbook can have a steep adoption curve because it has a large potential pool of classes to convert. This conversion process, in turn, has been formalized into policy and advocacy approaches, and has become an official responsibility of the library at many schools.  \\n\\n![](./img/StrangCalc.png)\\n\\nOA monographs, in contrast, come out of a more-or-less parallel but distinct movement to ensure that published research is free -- beginning with journal articles but extending to books and other research outputs. As research, OA monographs are usually specialized titles and almost by definition not intended to be direct substitutes for existing (commercial) titles. \\n\\nThe incentives and growth potential are accordingly different. Publishers are publishing more OA titles (measured by growth of the [Directory of Open Access Books](https://www.doabooks.org/) ), but they tend to fill small course niches and show up in classes that already assign a lot of other (commercial) books. The US and Canadian advocacy model doesn\'t apply as easily in this context, \'free\' has less of an impact, and the textbook-based curriculum of two-year schools is largely irrelevant.  This is, we think, the context for what we see in the US and Canadian data, which shows modest growth at best in the adoption of OA titles over the past ten years. \\n\\nBut elsewhere, the monograph vs textbook data is different and I don\'t think we have a complete explanation of it.  One important factor is that the market-size dynamic is reversed: it\'s entry-level textbooks that are fragmented by country and language -- and dominated by translated commercial textbook titles -- while the research culture is international and built around English. English-language research monographs \'travel\' better in this context and cost/benefit decisions shift accordingly.  There are also differences in the publishing and advocacy cultures that have grown up around Open Access research initiatives, with more active roles for libraries in funding new work.  We\'ll just note this for now without venturing a full explanation. \\n\\nWe\'ve alluded to a few caveats on the data already, and such a discu
1ssion could go on for some time.  But let\'s address one that matters to the OER community.  \\n\\nThere is reason to think that we undercount OER adoption -- though not dramatically with respect to anglophone countries. One source of undercount is our incomplete catalog of open titles.  For the current dataset, we combine [The Directory of Open Access books](https://www.doabooks.org/) (for monographs), the [Open Textbook Library](https://open.umn.edu/opentextbooks) (for textbooks), and parts of Academic Commons. (We also filter out public domain titles, which have begun to creep into OA/OER catalogs in new editions or translations).  This the \'high end\' of OER production in English -- most of it passing through peer review and/or press development processes. But these sources will miss a lot of non-English material relevant in other countries as well as less-formally-published work, such as material produced and circulated for departmental use or variations and remixes that are never recatalogued, and so on.  There are archives for these materials but every new source that we integrate produces trade-offs between signal and noise in the data, so we move forward with caution.\\n\\nA related source of undercount is the set of citation shortcuts that have grown up around some categories of OER title, including especially the replacement of authors by publishers in citations on syllabi. A typical example might list \'Chemistry by OpenStax\' on a syllabus rather than Chemistry by [some representation of the actual contributing editors and authors](https://openstax.org/details/books/chemistry-2e), which is our usual requirement for identifying titles.  We can do a sanity check on this \'missing\' material to some extent by [searching our collection for the term OpenStax](https://analytics.opensyllabus.org/trends?trends_query=openstax&institution_types=two_year). It appears on 1 in 125 US syllabi and 1 in 65 at 2-year schools --  ballpark consistent, we would argue, with our updated approach to these citations. But there are certainly other scenarios and cases that we miss. These issues also affect all OA/OER cataloguing efforts -- and we inherit the problems of the catalogs we use.\\n\\n![](./img/openstx.png)\\n\\nLoosening concepts of authorship are a challenge for our work but also, arguably, for a community that treats attribution as a primary reward for OER publishing. This is an inherent tension in the OER community, which also values the right to remix open content.  For our part, we think that we get the trend lines generally correct and undershoot a bit on the magnitudes.\\n\\nThe trend lines describe not just instances of adoption but also underlying *processes of adoption* that have institutional momentum -- and that momentum matters a great deal.  Textbook choice, especially, is sticky -- costly to change -- and it can take a long time for new titles to take off. The major OER math titles are over a decade old. The computer science titles a bit older. Social science titles and business titles have begun to emerge in significant numbers only in the last five years. And this isn\'t long in curricular terms.  Major commercial textbooks are often decades-old brands that become synonymous with their teaching subjects. \\n\\nLast year, we asked whether the growth rate for OER titles will be sustained.  This year\'s data provides a provisional yes. We\'ll continue to chart that progress and if we\u2019re right, by charting it, accelerate it.\\n\\n(April 18: updated with more thinking about the monograph data)"},{"id":"what-films-should-we-teach","metadata":{"permalink":"/what-films-should-we-teach","source":"@site/blog/2024-03-09-what-films-should-we-teach.md","title":"What Films Should We Teach","description":"Last year we published a roundtable discussion called \\"What Films Should We Teach: A Conversation About the Canon\\" in Public Books. For me, the goal was to spur some expert consideration of the Open Syllabus Movie Lab data, which I had assembled the year before, and which showed that the film curriculum in higher ed 
1is still mostly organized around critical canons and principles developed in the 1960s and 70s.  I wasn\'t equipped to unpack this data, but Jane Gaines, Jon Lewis, and Daisuke Miyao have built teaching and research careers around these questions and were generously willing to dig in.","date":"2024-03-09T00:00:00.000Z","formattedDate":"March 9, 2024","tags":[{"label":"opensyllabus","permalink":"/tags/opensyllabus"}],"readingTime":3.335,"hasTruncateMarker":false,"authors":[{"name":"Joe Karaganis","url":"https://github.com/karaganis","imageURL":"https://avatars.githubusercontent.com/u/5721537?v=4","key":"joe"}],"frontMatter":{"slug":"what-films-should-we-teach","title":"What Films Should We Teach","authors":["joe"],"tags":["opensyllabus"]},"prevItem":{"title":"OER Adoption Update: 2023","permalink":"/oer-adoption-update-2023"},"nextItem":{"title":"The Conservative University","permalink":"/the-conservative-university"}},"content":"Last year we published a roundtable discussion called [\\"What Films Should We Teach: A Conversation About the Canon\\"](https://www.publicbooks.org/what-films-should-we-teach-a-conversation-about-the-canon/) in [Public Books](https://www.publicbooks.org/). For me, the goal was to spur some expert consideration of the [Open Syllabus Movie Lab data](https://blog.opensyllabus.org/movie-lab), which I had assembled the year before, and which showed that the film curriculum in higher ed is still mostly organized around critical canons and principles developed in the 1960s and 70s.  I wasn\'t equipped to unpack this data, but Jane Gaines, Jon Lewis, and Daisuke Miyao have built teaching and research careers around these questions and were generously willing to dig in. \\n\\n>Jane Gaines (JG): ... In the United States, film and media studies grew out of literary studies and, at many schools, remains either within it or closely tied to it. The Open Syllabus rankings strongly suggest the continued relevance of literary approaches to teaching film history, which tend to bring the criteria of personal authorship to bear on film.\\n>\\n>Auteur theory received its major formulations by French New Wave directors and critics in the 1950s and 1960s, who argued that directors such as John Ford, Howard Hawks, and Alfred Hitchcock \u201ctranscended\u201d Hollywood industrial production and impressed a personal \u201cworldview\u201d on their material. The persistence of Hitchcock and Welles at the top of the Open Syllabus director rankings suggests how strongly this tradition is still with us. Teaching these two directors carries on a tradition of exposing college students to \u201cgreat authors\u201d because of assumptions about the depth of their personal insight and vision. And much of the critical tradition around these directors is still fundamentally tied to frameworks and vocabularies used to evaluate literary works.\\n\\nOne casualty of this focus is the collective and industrial dimension of filmmaking.\\n\\n>Daisuke Miyao: Cinema is a collective industrial and cultural form, but there is very little work that considers films as the product of collaboration beyond the auteur-director\u2019s authority. Much is lost through this narrow focus. The international impact of Rashomon, to take a major example, is inseparable from the contributions of its cinematographer, Kazuo Miyagawa, who experimented with film stock and lighting in ways that profoundly influenced a generation of artists in other countries, such as cinematographer Gregg Toland in Citizen Kane.  What would film history look like if, as a thought experiment, we privileged cinematographers or editors or writers?\\n\\nPredictably, one of the major factors that maintains the traditional canon is teaching bandwidth.\\n\\n>
1Jon Lewis (JL): When I arrived at Oregon State in 1983, there were only two movie classes: Film Comedy and Film Tragedy. Both were taught in the English Department. My colleagues were surprised to hear that I found the titles overbroad. But as a practical matter, they had to be. Only a fraction of schools in the United States have film programs: some sources put the number at over 300, depending on the definition; others say around 150 out of nearly 4,000 schools in the United States. This means that in most places the teaching canon is effectively whatever makes it into the Intro to Film class or its equivalent.\\n>\\n>Moreover, teaching time is scarce and presents zero-sum choices. In a semester-long class, adding one title to the syllabus often means removing another. The relevant question for me in teaching is: What do students need to know if\u2014as is often the case at Oregon State\u2014their film education consists of one class from me? My answer is almost always \u201chistory.\u201d Are there certain films students need to see in order to better understand contemporary cinema? Are there films that allow me to condense the discussion of a period or set of themes into one or two classes? This process of cropping is, for better and for worse, well suited to the auteur framework.\\n>\\n>So even as over the years I have been researching and writing about films that challenged the canon or offered new ways of looking at it, the traditional approach has remained useful.\\n\\nThe whole conversation is worth a read."},{"id":"the-conservative-university","metadata":{"permalink":"/the-conservative-university","source":"@site/blog/2023-07-11-conservative-university.md","title":"The Conservative University","description":"Much of the attention to \u2018woke\u2019 politics in higher ed assumes that the curriculum is a major driver of it \u2013 that students learn new ways of thinking about race or gender or inequality from their classes.  This is a topic where the analysis of millions of syllabi can perhaps shed some light.  At the risk of saying something both obvious and controversial, the curriculum of US higher education is very \'small c\' conservative, built around long-term, slow-changing approaches to skills and knowledge in different fields.  Our data suggests that it is, over the short and medium term at least, pretty insensitive to political and cultural change, and even to major shifts in knowledge and technology.  This varies somewhat by field, of course, but not by that much.","date":"2023-07-11T00:00:00.000Z","formattedDate":"July 11, 2023","tags":[{"label":"opensyllabus","permalink":"/tags/opensyllabus"}],"readingTime":4.4,"hasTruncateMarker":true,"authors":[{"name":"Joe Karaganis","url":"https://github.com/karaganis","imageURL":"https://avatars.githubusercontent.com/u/5721537?v=4","key":"joe"}],"frontMatter":{"slug":"the-conservative-university","title":"The Conservative University","authors":["joe"],"tags":["opensyllabus"]},"prevItem":{"title":"What Films Should We Teach","permalink":"/what-films-should-we-teach"},"nextItem":{"title":"May 2023 Updates","permalink":"/May-2023-updates"}},"content":"Much of the attention to \u2018woke\u2019 politics in higher ed assumes that the curriculum is a major driver of it \u2013 that students learn new ways of thinking about race or gender or inequality from their classes.  This is a topic where the analysis of millions of syllabi can perhaps shed some light.  At the risk of saying something both obvious and controversial, the curriculum of US higher education is very \'small c\' conservative, built around long-term, slow-changing approaches to skills and knowledge in different fields.  Our data suggests that it is, over the short and medium term at least, pretty insensitive to political and cultural change, and even to major shifts in knowledge and technology.  This varies somewhat by field, of course, but not by that much.  \\n\\nWe can test this claim in limited ways with the \'Trends\' dashboard in OS Analytics, which permits keyword and phrase searches across the descriptive content of 18.7 million syllabi.  What jumps out?  Well \u2013 restricting the search to US syllabi \u2013 few classes deal with the main topics of \u2018woke\u2019 politics at all.  Under 5% of classes reference \\"gender.\\" 3% reference \\"race.\\"  Zoom in on some of the more contested vocabulary around these topics and mentions become very scarce.  \\"Marxism,\\" \\"transgender,\\" and \\"critical race theory\\" each figure in around .1% of classes -- *1 in 1000 classes.*\\n\\n![All](./img/USoverall.png)\\n\\nLimit the range to R1 and R2 (research) universities and the percentages increase to around.... .2%.\\n\\n![R1R2](./img/US-R1R2.png)\\n\\n\x3c!--truncate--\x3e\\n\\nThe simple explanation for this is that there are lots of fields that only rarely engage with these issues. You can get through a STEM, health, or business degree, and even some social science degrees without encountering them in significant ways in class.  And those are the degrees that most students get.  Around 1 in 6 students graduates with a business degree and another 1 in 6 with a degree in a health field. [Around 1 in 500 students](https://nces.ed.gov/ipeds/SummaryTables/report/360?templateId=3600&year=2021&expand_by=0&tt=aggregate&instType=1&sid=d3e3eb30-c12a-4fb9-a0aa-ec56661f02e1) graduates with a degree in what the Dept of Ed lumps together as \\"Area, Ethnic, Cultural, Gender, and Group Studies.\\" From the [National Center for Educational Statistics](https://nces.ed.gov/fastfacts/display.asp?id=37):\\n\\n![Majors](./img/Majors.png)\\n\\nThere are, of course, larger fields in which race and gender are central subjects: sociology, for example, which accounts for roughly 1 in 125 graduates.  Each of the two terms figures in around 40% of sociology syllabi. But marxism, transgender, and CRT? Barely 1%.\\n\\n![Sociology](./img/Socio.png)\\n\\nHere\'s Political Science, where marxism has an obvious role to play. It\'s around 1%.\\n\\n![Polisci](./img/PoliSci.png)\\n\\nWhat about Women\'s Studies? Here\'s a good s
1anity check on our data, since one would expect gender to figure very prominently. And yes it does, generally north of 80% of syllabi. \u2018Transgender,\u2019 on the other hand, hovers somewhere above 6% in recent years. Here, as elsewhere, the stability is striking.  None of these terms has moved much in the past decade. \\n\\n![WomensSt](./img/WomensSt.png)\\n\\nWhat does it take to really move the needle in a field? I think we can see that to some extent with \u2018climate change\u2019 in the Earth Sciences.  Around twice as many classes mention climate change in 2022 as in 2010 \u2013 but it\u2019s still fewer than 1 in 5!  There are still lots of Earth Science classes about other topics.\\n\\n![Climate](./img/Climate.png)\\n\\n\u2018Machine learning\u2019 in Computer Science is another example.  It has roughly tripled since 2010 but only to around 6% -- still a narrow topic in a large field.  \u2018Artificial intelligence,\u2019 over this period, is steady state at around 2-3% of classes.  We\u2019ll see what 2023 brings.\\n\\n![Compsci](./img/CompSci.png)\\n\\nCan we rule out that our data is just insensitive to short term trends? I think so. Presidents and pandemic terms are very-low-frequency terms in the dataset, but they track pretty well with expectations.  Covid-related terms would be nearly ubiquitous since 2020 if we included the administrative sections of these documents \u2013 but we don\u2019t.\\n\\n![Presidents](./img/Presidents.png)\\n![COVID](./img/COVID.png)\\n\\nObviously, singling out a few keywords is a very limited way of looking at the content of the curriculum. One should expect concepts and perspectives to \'spread out\' across a wider vocabulary and in ways that are not always explicitly signaled in course descriptions and learning outcomes.  But testing with adjacent terms didn\u2019t turn up major divergence from the examples above. \\n\\nMy own experience would attribute more importance to these perspectives on campus and in the lives of students than the course data suggests.  At the same time, I think that most public conversations about higher ed rely too heavily on personal experience, elite colleges, and cherry-picked examples that are pretty remote from the experience of most students.  One could conclude from our data that students in 2022 are receiving educations that, in rough curricular terms, in most fields, would be hard to distinguish from those in 2010 \u2013 and probably earlier in many fields (we generally use 2010 as a starting point for time series views because that\u2019s when our dataset becomes large).  Much of this consistency is built into the structure of American higher education, in which change has to diffuse across hundreds of systems, thousands of schools, and generational processes of faculty turnover.  In short, if \u2018woke\u2019 politics is a force on US campuses, it\'s hard to see the curriculum as a significant contributing factor, much less a driver of it. You\'ll have to look elsewhere for explanations."},{"id":"May-2023-updates","metadata":{"permalink":"/May-2023-updates","source":"@site/blog/2023-05-29-may-2023-updates.md","title":"May 2023 Updates","description":"We\'ve made a number of changes in the past weeks to make Open Syllabus a better experience for users and to move toward a model of community support for our work.","date":"2023-05-29T00:00:00.000Z","formattedDate":"May 29, 2023","tags":[{"label":"opensyllabus","permalink":"/tags/opensyllabus"}],"readingTime":1.35,"hasTruncateMarker":false,"authors":[{"name":"Joe Karaganis","url":"https://github.com/karaganis","imageURL":"https://avatars.githubusercontent.com/u/5721537?v=4","key":"joe"}],"frontMatter":{"slug":"May-2023-updates","title":"May 2023 Updates","authors":["joe"],"tags":["opensyllabus"]},"prevItem":{"title":"The Conservative University","permalink":"/the-conservative-university"},"nextItem":{"title":"What\'s in a Name","permalink":"/whats-in-a-name"}},"content":"We\'ve made a number of changes in the past weeks to make Open Syllabus a better experience for users and to move toward a model of community support for our work.\\n\\nFirst, we\'ve created [a new landing page](https://
1opensyllabus.org) that links the various Open Syllabus sites and services.  We also switched our blog to a static page framework ([Docusaurus](https://docusaurus.io/), if you\'re curious).  \\n\\nSecond, we\'ve ended the open beta for [Open Syllabus Analytics](https://analytics.opensyllabus.org).  If you already have an account, it has been switched to a 30-day personal trial account.  Access via these accounts will time out unless you -- or someone at your organization -- creates an organizational account that you can join.  For more information about how this works, see [Account and Subscriptions](https://blog.opensyllabus.org/about-accounts). There are a number of subscription types and pricing tiers that hopefully can accommodate most schools and organizations.  Moving forward, we will continue to expand both the free and subscription services, with the latter providing more data, tools, and views.\\n\\nThird, we\'ve updated to a new version of the syllabus dataset (2.10) in Analytics. 2.10 adds around 3 million new syllabi, bringing the total to 18.7 million.  It especially expands coverage outside the US, with a focus on Europe.  \\n\\nIf you enjoy Open Syllabus, we\'d encourage you to create a trial account on Analytics and explore.  If you find value in it, please give some thought to how you can get your school or organization involved as a subscriber and -- especially for schools -- as a donor of syllabi to the archive.  We are building a global curricular archive for higher education and we need your help."},{"id":"whats-in-a-name","metadata":{"permalink":"/whats-in-a-name","source":"@site/blog/2023-03-06-whats-in-a-name.md","title":"What\'s in a Name","description":"There has been significant progress toward gender parity in many academic fields \u2014 and beyond it in some. The usual starting point for looking at this question in the US is the NSF\u2019s \\"Survey of Earned Doctorates,\\" which provides an annually-updated account of trends in PhD education. According to the most recent report from 2021, women make up almost 72% of psychology PhDs, 68% of health science PhDs, 53% in the biomedical sciences, and 50% in the social sciences. From there, percentages drop quickly: 33% in the physical sciences, 28% in math and statistics, 26% in engineering, and 25% in computer and information science. These percentages are not a measure of later employment in those fields \u2014 in fact the reality is probably somewhat worse due to \\"professional drop-off\\" among women in the STEM fields in which they are a minority. But it\u2019s a place to start.","date":"2023-03-06T00:00:00.000Z","formattedDate":"March 6, 2023","tags":[{"label":"opensyllabus","permalink":"/tags/opensyllabus"}],"readingTime":6.27,"hasTruncateMarker":true,"authors":[{"name":"Joe Karaganis","url":"https://github.com/karaganis","imageURL":"https://avatars.githubusercontent.com/u/5721537?v=4","key":"joe"}],"frontMatter":{"slug":"whats-in-a-name","title":"What\'s in a Name","authors":["joe"],"tags":["opensyllabus"]},"prevItem":{"title":"May 2023 Updates","permalink":"/May-2023-updates"},"nextItem":{"title":"Closing the OER Information Loop: 2022 data","permalink":"/closing-the-oer-information-loop-2022-data"}},"content":"There has been significant progress toward gender parity in many academic fields \u2014 and beyond it in some. The usual starting point for looking at this question in the US is the NSF\u2019s [\\"Survey of Earned Doctorates,\\"](https://ncses.nsf.gov/pubs/nsf23300/report/field-of-doctorate#field-of-doctorate-trends) which provides an annually-updated account of trends in PhD education. According to the most recent report from 2021, women make up almost 72% of psychology PhDs, 68% of health science PhDs, 53% in the biomedical sciences, and 50% in the social sciences. From there, percentages drop quickly: 33% in the physical sciences, 28% in math and statistics, 26% in engineering, and 25% in computer and information science. These percentages are not a measure of later employment in those fields \u2014 in fact the reality is probably somewhat worse due to [\\"professional drop-off\\"](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0267561#pone.0267561.ref015) among women in the STEM fields in which they are a minority. But it\u2019s a place to start.\\r\\n\\r\\n\x3c!--truncate--\x3e\\r\\n\\r\\nThere is a parallel, widely held but harder to empirically document argument that the role of women in the curriculum lags their presence as faculty and instructors \u2014 and in some fields by a lot. Here, Open Syllabus can offer solid confirmation. The new Gender Statistics dashboard in [\\"Open Syllabus Analytics\\"](https://analytics.opensyllabus.org/) explores the question of author and instructor gender ratios across the full syllabus collection \u2014 some 16 million in early 2023. The most useful view at the moment is of the 62 fields into which Open Syllabus classifies syllabi. The results are striking.\\r\\n\\r\\n![Fields](./img/fields-author-instructor.webp)\\r\\n![](./img/fields-author-instructor-separator.webp)\\r\\n![](./img/fields-author-instructor-continue.webp)\\r\\n\\r\\nLet\u2019s pause for a moment on the methodology. Open Syllabus is building on a tradition of work that infers gender from first names, based on assigned gender ratios derived from censuses, baby registry databases, and similar sources.* This method is surprisingly good in most cases. Most common names are assigned to men or women almost exclusively. Some, of course, are assigned to various degrees across genders.\\r\\n\\r\\nThe Open Syllabus dataset is divided into four categories that capture some of this ambiguity. Names with a high degree of association with women (green) and names with a high degree of association with men (dark blue) are the main subject of this analysis. But we also break out \u2018ambiguous names\u2019 where the association with a specific gender falls below 70%. And \u2018unresolved names\u2019 that we can\u2019t interpret. Citation practices that use initials are the most common source for this category.\\r\\n\\r\\nAnd of course there are other caveats. This is a binary model that doesn\u2019t account for non-binary gender identities. That\u2019s a category problem that we can\u2019t account for here. Name usage also varies by country and over time. Name analysis sometimes makes a mess of romanized Asian names. First name \u2013 last name ordering conventions vary and have, for some countries, changed in recent decades. There are, in other words, many limitations to the methodology. Yet with all that, the data tells a compelling story.\\r\\n\\r\\nThe best way to read the data, we think, is to compare the ratio of the \u2018strong\u2019 name categories \u2014 green and dark blue. The size of the ambiguous and unresolved names categories will then introduce more or less caution in interpreting results.\\r\\n\\r\\nThe main takeaway is that the ratio of women to men as authors in the curriculum lags the ratio of women to men as instructors in every dimension of the data we looked at. These differences track stereotypical notions of male and female dominated fields, with cosmetology, dance, and the \u2018helping\u2019 fields on on end and the hard STEM and engineering trade fields on the other.\\r\\n\\r\\nThe only fields in which w
1omen represent more than 50% of the assigned authors are cosmetology, nursing, women\u2019s studies, dance, social work, nutrition, and dentistry \u2014 with only the first four significantly above parity. And even here, assignment ratios significantly lag instructor ratios. In cosmetology, women authors outnumber male authors by a bit over 3-1, but among instructors by 11-1. In dance, women outnumber men 2-1 among authors and 3-1 among instructors. And so it goes down the whole list. In social work, the ratio is just over 1-1 for authors and 2.5-1 for instructors. In psychology, 3-5 for authors and 3-2 for instructors. In sociology, 1-2 among authors and parity among instructors. In political science, 1-3.5 and 1-2.\\r\\n\\r\\nAs in the NSF data, things get rougher in the STEM fields \u2014 with the partial exception of biology. In biology, the ratio is around 2-5 for authors and 3.5-5 for instructors \u2014 middle of the pack in the dataset. For chemistry, 1-5 for authors and 3-5 for instructors. For computer science, 1-7 and 1-3. For engineering, 1-8 and 1-2.5. What\u2019s the nadir among the major fields? Physics, with a 1-25 ratio of women to men among assigned authors and a 1-3.5 ratio among instructors.\\r\\n\\r\\nWhat else is of note here? The poor performance of the business fields, for one, with 1-3 or 1-4 ratios among authors and near parity among instructors across the business, marketing, and accounting fields we classify. The dismal performance of the engineering trade fields for another. Construction, engineering technologies, and mechanic / repair technician feature at the bottom (mechanic / repair technician has statistically 0% women authors). And let\u2019s take some note of economics \u2014 the worst of the social sciences, with a 1-7 author ratio and 1-2 for instructors.\\r\\n\\r\\nHow can we explain these lagging assignment to instructor ratios? The answer probably varies across fields. In the humanities and social sciences, the role of historical \u2018canons\u2019 almost certainly play a role. The history of social thought or literature by definition passes through centuries in which women were had few opportunities to contribute. In other fields, however, teaching is skewed toward textbooks, and accordingly to the more de facto gender politics of the textbook industry, which relies on author-brands built over decades. The top economics textbook authors are, with no exceptions, men in their 60s and 70s. Campbell\u2019s highly-ranked Biology was first published in 1987. Elaine Marieb \u2014 one of the few women to break into this circle in the 1980s with Human Anatomy \u2014 would have been 87 this year. Brand investment strategies and the stickiness of the textbook marketing may be the main contributor to assignment lag in most fields\\r\\n\\r\\nWe are exploring other ways of viewing and navigating the data, and these will expand over time in the dashboard. It\u2019s easy to aggregate at the country level, for example, and provide a sanity check on the overall composition of the collection. Across the anglophone countries, gender ratios are quite stable: among authors, 28% women in the US, 27% in Canada, 26% in Australia, and 25% in the UK. We\u2019re less confident about results from non-anglophone countries where we make few accommodations for variations in name use.\\r\\n\\r\\n![Countries](./img/countries-author-instructor.webp)\\r\\n\\r\\nIt\u2019s also possible to sort the data by school, and we will provide more detailed school breakdowns when we have good recent syllabus coverage. This, hopefully, will be an incentive for schools to join and contribute to Analytics, with the goal of building a global teaching archive that can support novel views of the collective dimensions of teaching.\\r\\n\\r\\n*Among the papers that contributed to this work:\\r\\n\\r\\n- Santamar\xeda, Luc\xeda and Helena Mihaljevi\u0107. \u201cComparison and benchmark of name-to-gender inference services.\u201d PeerJ Computer Science 4 (2018): n. pag.\\r\\n- B\xe9rub\xe9, Nicolas et al. \u201cWiki-Gendersort: Automatic gender detection using first names in Wikipedia.\u201d (2020).\\r\\n- Menendez, David et al. \u201cDamegender: Writing and Comparing Gender Detection Tools.\u201d Seminar on Advanced Techniques and Tools for Software Evolution (2020).\\r\\n\\r\\nThe principal engineering and data science was done by Mingi Ryu."},{"id":"closing-the-oer-information-loop-2022-data","metadata":{"permalink":"/closing-the-oer-information-loop-2022-data","source":"@site/blog/2023-03-05-closing-the-oer-information-loop-2022-data.md","title":"Closing the OER Information Loop: 2022 data","description":"A few years ago we reported on the rates of growth in the adoption of \u2018Open Educational Resource\u2019 (OER) textbooks and \u2018Open Access\u2019 (OA) monographs. These are books published under Creative 
1Commons licenses, which means that they can be used and circulated freely. In world of $200 commercial textbooks, OER textbooks, in particular, have become an important part of school and state efforts to reduce student costs.","date":"2023-03-05T00:00:00.000Z","formattedDate":"March 5, 2023","tags":[{"label":"opensyllabus","permalink":"/tags/opensyllabus"}],"readingTime":2.82,"hasTruncateMarker":false,"authors":[{"name":"Joe Karaganis","url":"https://github.com/karaganis","imageURL":"https://avatars.githubusercontent.com/u/5721537?v=4","key":"joe"}],"frontMatter":{"slug":"closing-the-oer-information-loop-2022-data","title":"Closing the OER Information Loop: 2022 data","authors":["joe"],"tags":["opensyllabus"]},"prevItem":{"title":"What\'s in a Name","permalink":"/whats-in-a-name"},"nextItem":{"title":"Hits of the 1990s","permalink":"/hits-of-the-1990s"}},"content":"![](./img/syllabi-with-citations-titles.webp)\\r\\n\\r\\nA few years ago we reported on the rates of growth in the adoption of \u2018Open Educational Resource\u2019 (OER) textbooks and \u2018Open Access\u2019 (OA) monographs. These are books published under Creative Commons licenses, which means that they can be used and circulated freely. In world of $200 commercial textbooks, OER textbooks, in particular, have become an important part of school and state efforts to reduce student costs.\\r\\n\\r\\n\\r\\nBut free has a few drawbacks. In markets for commercial textbooks (and most other goods), supply and demand are connected by the sale. Producers and consumers communicate through this information loop, and this relationship makes the market more or less efficient and \u2014 on the supply side \u2014 capable of adjusting.\\r\\n\\r\\nThe information loop for free digital books, on the other hand, isn\u2019t closed. There is no sale or single point of access and titles are copied and circulate freely. It\u2019s hard, accordingly, to know what the demand side of the OER and OA ecosystems looks like. And this lack of information becomes a problem for authors and publishers (the producers) and faculty and students (the consumers). Decisions to invest time and money by faculty, funders, libraries and others in creating new titles are made without strong insight into the demand for existing ones. Adoption decisions by faculty and staff are made without much visibility into the experience of other programs, which could provide models. Both sides of the equation involve risks, that those risks are hard to mitigate.\\r\\n\\r\\nOpen Syllabus can construct this demand side information from syllabi, and so partially close the information loop. The chart above continues the story we began to tell in 2021 \u2014 one of roughly 20% annual growth in OER adoption since the early days in 2013 and 2014\\\\. That\u2019s fast growth, but from a very low baseline. In 2014, around .2% (1 in 500) syllabi used an OER textbook in the US. By 2021, that number was around .8% or 1 in 125\\\\. 2022 data shows a drop, but current year data can be uneven due to lag in our collecting practices. You can explore all of this in detail at the new [Open Syllabus Analytics](https://analytics.opensyllabus.org).\\r\\n\\r\\nThere is a lot of variety in adoption across countries, states, and fields. California is a national leader in OER adoption at nearly three times the national average. Texas lags the national average by about a third.\\r\\n\\r\\n![](./img/syllabi-with-citations-california.webp)\\r\\n\\r\\n![](./img/syllabi-with-citations-texas.webp)\\r\\n\\r\\nCanadian OER adoption looks very similar to the US but UK adoption is heavily skewed toward monographs, with very little traction for textbooks.\\r\\n\\r\\n![](./img/syllabi-with-citations-canada.webp)\\r\\n\\r\\n![](./img/syllabi-with-citations-uk.webp)\\r\\n\\r\\nMath is the field with the strongest uptake whereas in other fields OER textbooks have only begun to be available.\\r\\n\\r\\n![](./img/syllabi-with-citations-math-us.webp)\\r\\n\\r\\n![](./img/syllabi-with-citations-political-science-us.webp)\\r\\n\\r\\nBecause textbook use is sticky, it can take a long time for new titles to accumulate momentum. The major OER math titles are around a decade old. The computer science titles a bit older. Social science titles and business titles have begun to emerge in significant numbers only in the last five years. Major commercial textbooks, in contrast, are often decades-old brands that become synonymous with their teaching subjects.\\r\\n\\r\\nThe question for the next years is whether that growth rate will be sustained. If it is, OER will begin to take a significant chunk of the overall textbook market. Open Syllabus demand metrics will chart that progress. And if we\u2019re right, by charting it, accelerate it."},{"id":"hits-of-the-1990s","metadata":{"permalink":"/hits-of-the-1990s","source":"@site/blog/2023-01-15-hits-of-the-90.md","title":"Hits of the 1990s","description":"Hits of the 1990s","date":"2023-01-15T00:00:00.000Z","formattedDate":"January 15, 2023","tags":[{"label":"opensyllabus","permalink":"/tags/opensyllabus"}],"readingTime":1.685,"hasTruncateMarker":false,"authors":[{"name":"Joe Karaganis","url":"https://github.com/karaganis","imageURL":"https://avatars.githubusercontent.com/u/5721537?v=4","key":"joe"}],"frontMatter":{"slug":"hits-of-the-1990s","title":"Hits of the 1990s","authors":["joe"],"tags":["opensyllabus"]},"prevItem":{"title":"Closing the OER Information Loop: 2022 data","permalink":"/closing-the-oer-information-loop-2022-data"},"nextItem":{"title":"Open Syllabus announces new support from the Arcadia Fund","permalink":"/open-syllabus-announces-new-support-from-the-arcadia-fund"}},"content":"#### Hits of the 1990s\\r\\n\\r\\nThe Pudding published [a fun and interesting visual exploration](https://pudding.cool/2023/01/lit-canon/) of which titles published in the 1990s have become part of the teaching canon in higher ed. The winner by a very large margin is Tim O\u2019Brien\u2019s _The Things They Carried_ \u2014 a book of linked stories about the Vietnam War published in 1990\\\\. _Woman Hollering Creek_ by Sandra Cisneros and Arundhati Roy\u2019s _The God of Small Things_ are #2 and #3\\\\. _Harry Potter and the Sorcerer\u2019s Stone_ is #4\\\\. Here is how those titles track over the past decade (this is from [Open Syllabus Analytics](https://analytics.opensyllabus.org) \u2014 which you can test drive)\\r\\n\\r\\n![](data:image/png;base64,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1NlXpdQ9qHJ9lJm4S27jvQvhKlcOwGt3g+U6fpAinWe2A/UuOcxnKf8wTbe87A5j+xt4ujDXebPs11Uzz4uq2877FcE2jqZPpusBO6SquOcwjnvGpxU7/qi1vsdGd+5nOhDZqHb+mznvuNwz+Wwmzpu+2I2u/FW0Adon2fmvotZt5YtRt1NNj51uenYPuhHfFYqSbVpc+vahfu22lPxjJdjdm27TDTJWBjWyS8uWwvlLJ9h4Ph8x3jDrr8gnWHuaLdindn4y1YqZcfoHaUyudY97Cg018qTsOdw+Jw6WzHn3izTlg2atgTdVkpiaJVmpu+GibBsNXHXD876MSttnT1C6x7yo5UppuOvfmBSINbd3aoAciQtFofUA3ahK7lh7Ld1onW2udAfGqN5arOiXHQzvZC9h84ZGdUKn+d5HCEyO/yNkZxRGsEeFDzLarJu2nkczRM423f73HbAq3zOdpdi2xHJrNdGyQed+AEwt66yttw7uostiTmW1FirSWDdJouzoqpmoL6VfM5fDKL2ElhuZUo/Sq4Ul2LLWhdthVU8MtVFQjP5yYLidKwpUFI+Tx6Kcen8fDW7k9bWEpn7I7il8eS2bNqqLfinQKOau5XNiYxG797ZRmdnRg688fPx2ei1kGwpKIa87npkrmJed5fQeU7JF3GO0581o78u1Kqvfnif14POubrwd0/7MV8JU8/FhaGc19Ctf8kbSRE98HrEiy826PEhGkLFox8r624O+tFcP/Sr7k1DFJXkkZWIRBcBZ+PRn2OMeHxxQ0wqnhiDLnSl7RpP2W7BTTFWu9yZRHoek6Iu+ZJvMdV7jy7ERaLTffEYI10E1kUG9aXZeElT9ZLx1ONidIE9xdpFirqALz71uJzZ5YhlxNdLVZjibQ25852DHIMupszraliJeHM84/Nkx5Jj0Xj1f2tt41tGFmS0hlos3icLyceFFvmoP3lW2RFMdtXYOiQ7b6tq6xoyXst5sSjmfN6UKJMX4fOikBMmLDFsZEsMLOc5m1d9ZmBVTWRsfbwx/vzPLFSPPx1jUehlHVfelhwTtUTbVoU1i+pcx7bUsYy4DXuWmfkLednSXmtax3hIpLDi13/aNMeVvFVjEfNxvA4bki2bOkY87+MCW46yxCW62BbzvKyETV1YtO0xhjG+iFPx5p+Yy3TFvnCiStj2GLOcvlpQeV7UBb+au7ygl4kUumheFwJ9hw3b9frr/uV3BGaxxb6vm0XrqKh/gaDNiSTO0h1WTi2JW8kOn8V49mUZSQ657aHO/Z4GrSVRnqfG628a3mJ8OU9t3nMNpG/FJkux72usjvM6N+sCcFvLWjexaY6eeuL9PWlDx6Hm1rH1dbAYZKoOGdnJpnpNH/38pvNc+ZKI3z9TXC9hV//lFjuX26or/h1OyZewXpveAuro3S/Z6n7P3CJzHm7ynP1HuaxUskA+6ivGp7saPvjgQ3FxP+7WGAb0uLr58ax5/kjDbYwaaztX+LOHLppXAmG93+cdBjWXoZdJN/GZRevu+bjg/fjj/5SJObpwr+RiPaJKfn0HLb23KTHl2Uhky4SiPP/6+JfvIO1BtsGbfVYbWnNR6Ircr+W3kp3qzkB6pG8lPOVY2/rzWjCnWp/lu2JQwpM+4ywd6r1fnx3yMVJRzq3AV3klX42nvNpIG0vY0fuj1nwmnCnh6eknk219/no2jplI5sjkiJqHShApO0oUlJ62HE/jXGOrc2XOc7ZXPQTbePVeUXrSz+WRYTm2tKqX2Gq9VXJ8HcP1iL9KCDfLNcf8LFtOcvbEv+bACU8Vu+zLtm3eH59nH3rBh+TnZAWZiRbiFI9ZzrtaxZjndVV+5/czj0tjjQS2mMNKeNK5QMz1vjklPIXtOdZR1ufwxquwJI20Eecqc5YPtXnfhPpYY9RuCimJLoylDzVbqplTUzWu5IekBJa21HKZrY61vRDD5NqPo5W3dd+IQGPPOW9zf6++q+g3oqhrn+tZx6ushJnlGsrWbl99uYb0PpL2lNxZSU0x+/nbU9lTv+50F8dxP35rfTgyfz6uOazvXXku1LESf7XmdE5UEqfmWt9dYt1EXwhknHUOrfWheHS+keMql++R9FsJqpJxIpTOVf48Fm+abUzFQnHOLOZzs/z6zqmSqXN3PRpWY9D3K8tXjBVvGFywTBYbU6f4PQ6ttTz/hLBsiofu7ufP9fpeo3LZTot7L2McG85COsOatOR+3TZ1T8Vte10gupcSy1qXO1LQCpy3YaGVRsMsNpWPC2yNU+Nfb0NO9rTGxOi47TH62aB19F2/ynPv7HdIutUtVV/UouK4l2HZ+kLaBjWUjS0aZWSzr8R1lPj7ebbMsqHrYzP7wlSOtcnkERbBHrzDk0Kd7ZXLq3xdwlgCs58h41LGlBW3WHZrP8lMM6PW2zu2rVi2l+KeZItTk3UdMe7F0xpM8lIxaWxamGHTdq7E7qGgT2yf4+kq03rpbR8Ihcb4qkLVcWkUV2XzKuz0Obvi8V5FbKfa8DlvzbeP7URDktfmY6pq53y9TRwzthbgepxzhH0MGUd7s9KYThqUhWzlxHPtHMBZZQfiSTtLPvodjaM7QeW2iyimS8UzzU9H0qK+lN1zjPykMWSc9V5j03cqPvvb2p+yJo7KNOjrD64nMdkK6GjbAWJbzQeD3hI+6nTRmR9Up5blp4fqWHvooaw7JH60s9lreocO9ez24i+V/rr1fmk7B1SaroOtAPMLaLc6Clv23bvsW9qzzCWP/mHmpFKHfUBaX1IsU+f8uX5A6YLN8mMmFzSxqTbivyr7h+a+3C/HsF5Txlk2xDeiWguNg6CPyCJL/ZqTLhQFyZW9+vKcP+a0H7M8d/v+hoW1n9Hjkn7sjKAjENmrP/1rXPdXwXF6Snu3xbQfSnNrlpfyrbZs7OO08qI7bNt8raxFb++LIPJ/2agv3bYWey8dq5ahSeCcxR6QDYe+284w5RAWYk3X86q+xdz2SciOUnXbCX7tc4q2bOSre0eTTdaaUHvTlE8V1ypbbcPc2SU7tKT8rNvmvl52oe3nuJr+guMkbtaH+ifRXtySPTYN9pF7C8raXmzTGhfLIVKVC7za3cBoOOXA/WW6OVVjKGyN81AIHuPcv9Xm/mUU0epAHGiLoctHe+fXwtxbf01YcWeyh34cj016ix/f2w/u6VJ9+acLNJUgpH9ZnuPv9lrCU/ywL9uSq3/9Xrq6m5KSnhy61OpH93p0iC5Y5iYB/YX/vPNRJhSMpBzzqn0MUgGG/JiHoqZ6JdREXLoLQFwo1fGYeqFTP5i3OzzFHT36Jt9bm/y0LUZUJe+ir/yFz3Yh4d6W7FR3fagLKxVnYy178af/lPBUP9bX0G80hrIpRrogM3tP525o4U7h1RgdrDWbQM6j5jb6c75VnpW7nnyHcfOQjMqtnuPtsmquQHpYUU2z8WL73stG59XKZUrtNloFy7nDfrpUl3eL9qNRPzjrolFeMIo16U0SSoioC7n1r+Crr+bScrlvg/K8L2LwgJuCZWqsGnt01Mvy+GpczCQv3EScZ27dXxQGrIWa4kuX0TpErDi4Ssn+y4D7Ws3Ksa8LYiNpQ3rW9YW58hntuaa0roadMlXzrqB0bPoimxMhnKCSWg5XlQxrGVs1aq4UU/RVd9RbIY20FwXS4i2zVR8ik74NuTPtlc1aS3FROtaRxqiL3ZkQEBci82Jku/js2GRCjLT+uv+EMg/OjpZ7s122Vk0mtoap3jS/pXRym2Mzx6rPPs14HeMsY3eH4unxh3kfM91jC8F+5imxz1hB5SJ2kit7tb5cVqPOn+qvi7tK2qhjvXSkHP5jfp568v05lzlfeTF1XDzNOGTUg4niYmvxNlLR5Tjm/Th26sJ1U9LobbcZVd0uepcaQsWy7s/GGnAPKeOVeFPWziLVZ23HINmu3mWrJdi1i/WpFS/5vqAxxn/Js90lRO9T9z2ghKd6v9MF6rrQLw4xDy0BSM4Um3zWXue9qMc85AX+3Csg/RVDxSJb8pfHXlzcfuLx98RnhKdybpVc/MBDkfAUW53rldAcx+czT2Yim8+v0vVWvsPHCkdEUiJtl5WQsVitByUy6E4ucRfH++IuT3E3IN2NSWPv57WA3s9tKk9/8l13lyr2eY5ogXn+aryypz99rnLkLTwBvPAmY9bXOaoSwPUZTZ/bdIenZ556os5vcTzU/PlYigSRTFCvgDQH+nyX1jSvwdhsc5yu5771uS3HoLVQsWiMLo/hOc5KohvJJJUApXkIpeaz7A8sei/UugobkomOmoeS0LrIu7m1diWuaI1pHu+JMerRZ5mwruSSiDk/S4hRjLfWmRJRmv3G0/OnfY2l4tcc3htrRY+4LC7tfUMDjT8lcqzZSU7xh0DZKlO97v7yIzOS09i6YI5XLY7LXdYp6SbfdjkP2RENXnhlWK1t84Icvtxz1l6+ezwHhGstrDvXvhyD5NZ9QzeOvvIXRuV3TnjSfOs4zjtFNpUcapqWpja9DvuyUeez+ByjzzJ53Nd3H9nTOsrPJqGWx67PfbKkgclaOPHn0zwvRd1rqbdr7eo7l9aG1mD+1VpQ2TGlragpQbTueBk+0netZ/ms71v1/uf3xfq+Usd1BKaw2nhdsq8au+Ko5HbFr8/VYw3rkbCZbPpcjSuPj/xsZBsRb4JNN/mSLvWS5mtuWhjJKVlFg+NXdPldMr4z5XlH3w8VUx6T4x8MDA/LUq35MY/L3sO10htzZ0m1O14PzfUh49Lt2LexrIdUU+nlEY7XAoqlhDy2Ed3hY9NjlL15HQ/djZKUHE8vOJ4RQxdZmZDk3Berqdd7PBaQq1bufX3s6tCffU+O7MR23OV211d7MejfQVZ9Wut6j+9rPurdc/gJzdQ4mPC0tnf19ZpELYABa/bSwx2NbRBrTiWwL79td5i7W0qayP0D4Wqi84Eia6f6uEw8l9G9mhHfHiu3e1zH5unqfesI2j9ebg+567F69czmcdw5frdrHFdtV+dav/nNpCjffgJXPZe3P+Kr93C7Gci+t9v/Pnrnzi8e0/G9PmiOz5KSnXAcUB28SkDvN/t22ufQTRv6kFofW4etUVq+g2X73DlbLNdzy4ZydcvEKe+M83pYGr5szYzGlxxZPHZ+9efc43NiOFswKuatOS7bh3X2R3uInv0vNcaxpO8Bh74LGEDTnV3Mg3b7tqu141MW8VJHtfOg2Nc+qaWGNK/E5YC25smGt9fCUn+s8HX7QDLs7H838drXPv/iByjtPZfud0zppbmyXfft72MNxA+m+k+LPm2erRRmykGxs9XmtO3cuth3hX0hj28h3yt1nPbquiA26zbX7dPjcr31r/XmJdfL0rH+iXYttrXvPpvd9NPiOjSvW3bOahtUcoabePfe1VdIersKjme9lxXF7W1hNQxafrO/N4ZW49pZq88Bqc9l62jvubAf19XX5DOeKPfjY+FAghfdlmtxK8S5rUJrx5ZcrmJ1fN6XSAW7NfQ5avXP5mz+kJ58OJ7ZTvcpt5rVkJvjUWvpRl/KaNcK6uufHSRZ23r+3b659yAOBR5KaS/kbFfxZIzSUXvIeM7rR++R8JTRNl39iOyLcGmrXayqH+R18ScspaGKVBf5lOSTd9aL9pSLH6Mdhx9V43FVXLpQoItA8a/qIynJcWqvCwDP6u4M8a946+4H+rHf2uI8yiqlbjbVD/v+IV9j1J0n6qJVm40wpB/I65Ek+gFfcdre/mcccUmB7lR1y6ur6US/LypkolP41YUUXTxTwPlfi7suVsiMYqkf7FWWjO8EIQ950TMulGlzjEEnaksAbWRpL5lbWC0Sj5cMOfaeV9nMcpdVPMXW8yOOaSDjb525U6TTlpVFS3Q235OYi2W/5AvrrDvKKWdPEhxdUZwq4WusAXtR6DVeXwT3wklTcaHqnryYrOSbkZAgbXPolqIh+fUGFya+yVh1CdeP3S6nbl6sKj21Z1+zmp8vpNeGpN3szyN123b/Wnm2V33Sz1JBV5jpKavdeMkWz6Acnf4rad0Zoz26JMdRF4NtKy/cqjKcGXtyTlvB23fYyguCcR6oRIG4+GYIchabfY/wLDHWl/qKpzSa702CsicZj3GU036Ox55kU+edqucFvjhv6JjR3e7yTgN5ZxbJ1MVwy8pqNKbumGfHO+ynXIrW+tVYJa//FuFbcG9fNmtM6tS4HO+ecLJU6yLOJiYbbaiSaK3eFS/X9vtHz1wa9sy9ekf72o9GUL6SwWwsyuKjTfH3iFqb10kmZ2RbrVtNeCYb6HFjkWyQ663pqE9z6cd3ya6TPzqjJmvfGUC8yH+PsQ3Do7H5HE0G2qINgVxTE1+PN+16ULY3QJW3bK/OrjfZl98edxocL8UnznM9OAUz+lVKru6PvVhlwrAu6OvC/r2VgCsbspeJIXHs1jijLc+jNQiZUckhd1aipnG1jsUQW0Dqt57iqq30si+ON82Z3sdzbuMzxAMPPhh2I+EpPncoETHfV+Muk5WUOJ+vmn+brXBHPGpvQWUMTU5i9WjEYBAJDffeE8lOkeikx9dq3z9Pic1ydeR4fS6RnVxj8RlLazU/o+T5T3dzrPWpc2Hai/pYzy2QtjM3cU6ey+6Ta9b3eU3JQWL7lBKe4i5GuptW9gXz5KL3zfx+XS703irm3hRXzZE5j3NjMmifJfMzZK6FdfzWK3eyW8fejfg8dV/8Fetqb8ka07qpOGpSNTYn6ceBnnGlLXXEpmNf66M2refiHxOR692PKAzJHLrGlceR5idkopLvhfLRx5axlHzKpq+az/qcq88adZ6pu6bWeDMRMuIZ7CreMbcCq0j1ovhlo3SHTgo0mdjFVuzGftir/nxtamFtaoziXC1k0TY3TuIa56G+SayKsmGD1XKa+tK3jrPRMkrm485kkOem4qDHFvpzufa6Q5vOI5pPH8c1vbUOdFzWvNdeMikX/jPhIYTXnD1krwvXNc/5eLc4DnTs6G5hPmbqOItxVEOotDG1tSYf9i17WY9zR50jlFylY7PGUXQVl9ZpSqe841SLtlw7+j4XvuocpeO16j5P5ZqJmCohMv6BiOxFXevXfdK1Xtpsx1v2l3XRikNLzGQgjuf2KEDZ0/Gk4y1ZR2+Os313qvOh3mduxXkpzk1PPLF78on37p5+Mh67qXN9nKf0qMkcqyyn/Si0zTFIX5vweivR+tzltsP7STGFEsQkvqpLfNG01m+qCmIOarK4Lmos8/BmNbePNgcwW3EMCiw2C7dqNU6vFldTH8vcOMmeWbTe5HsK2r1ppomsw9I5SutIwXhea74X2n1YslV45/5ag9ZPfynYS1UQG8cXLatqE64IU2xykcU8BiabacD1iuGaEp4caQW/BOE+Bxr7Gk02qDgmZch6HU1aU3HITY13TVELaMng6kI7fDI67OOi8djX7RrL4Yhvf8/tHpvtayRrfhedj8NUdATd3cfE4djP7jHLNcezNU+VuHP8btdYrtru9pvTqTyRuwyBqz8/XCaa69G96vW8HoXtq/2U84rlT5Fd+xqfcO6Oc/QYy4h0+lw6Ghel8SmtmjWWaNs6da5FU8GNZuD6wsmiMmKyrL5UlojHYIV5XtZ9ljm2t86wvy3dRp2dJ43Eg2iG5Sf1JkNjdM3n1LcdhbCXlr7AaHNd5WqJtrVh1aNzfOkZsqNUSk10sibL6232Wn2eh4wrxuz6QrNDbq0OOKvheTiPltbZB7OwVJVur2LfkDjcJPMXUDtscN+Y11Yfy9rhYrxl2cP10JYqax+HB2E7xuhFMc+L49NeP3roQrpDKk9rf2Nt9fgOAqk1oPWQPiPU4bvN7Z5u89rd9kJKOt7iGX1T9+ibGpt9rdbRv+e0Gpbmsm1B10Dnga/bXO9+l75me71snXPYXVpd1vrow655e+yqZ799LlVPrq2Pfs3xYjImS8dcOb713mNIqzZgPq1uHbmyfHe70tmLLvqtbzZdV34mfctlf2s3w66756Cv0G52Ltim9PXnukciNx6uB+cxrsJLs6XfjrNoWcQnH5PzlG1+m3Ltmox1577WlTEpts3NHT3wIZU+g5HWicc8eoW7MWgyi76MvlaY2wcvtxzZOx7HN4l2O01GPzxXfCEU8j0uVaUXbb5go2qOqOnKVv6r4vaDtWBlW79QFRdiojxPRv7QHj9e14WwuAgUuvox37rPZeLSuJNSxVbn6Xvi4oN++HacGWvY1w/Yuhigi5r1Q3xFqtdKjhgTKJ3aYly6eBiPltEP64pLjyjJiwBNQoe4fiRXwpMu6NUFL41xCNhetWky46/7CLkhHMXqk07+SB8XTirJSuPSv7CO5Nv2I3/aDflcBc1f/uvkZFs/5M6ydZeMuMNTk9U8aRtHQovbNmU5RLwetM9yM6Cy14bsZH/omJ6sq6w4K464YCLB2DL2iUHWq+uk11Lt1hY4lwZCponZR45XbZN/6RSHxsR9VU0bNd64EKOLsnFRMs02Oc9VrpdYJ7mJXZUWrpJha7dgIS2+izsztLn0xatZbpgIvai0YWZzynncTXBPZtJxnE20sXBtv9c92otrxaVyyY56BdHZN16uK/LxKJN2UbaNWfTmZEn7Kt2YrbB1QxeV9RlNFw3j2NCFxrqbSbUVFclWxKkT8h6R2ltXrd8IvNZxyPucJVW1x272reayV7Gorq0RCHXZinOb2qKxkgBU1x04ah3lBcsYb42zXfCLfsmWXos0gVZscpCtjiklx0uuL7GJv1prVphkRjFLYtDXpYEMMs3hUCoOo951W5O4ZshZrzHoNaIaSnNfifR5WgmV5AGZtZ9Z13Gu45PMus+RpZvGr45r9VSvdHKNtYvc9V7QNGO
1nY7eSZypBNxPZ4n2h+xeYyXdWVG+FWmVRaWOtXatY2Ptslkb1i22NydZC0MVmoseRWsPucl5kp5wMedWrMfvipS6M20F0j4lYjFfykhWr++IxdUp6uhHJoffGnRKVzOt5yMd/KVFAsUVo5Vvl+vPxqLred+b+VMgQ20gyPsU24kv5FqOOv+eeb8kp0ab3bR1zTnhSjLKpi/JKlM5+XQzX54eQq0TMOl/J7eGtGFesGtnYlCCri/L33hcsIvHmgUh00qPPlPSkdVfCcQwnj+Yrxx7+83OMILXHMsUdUaSjz0z3KzE6uGosej9SQtVyfkcMp5Qc8yB5upbG/UwkFTwZyQSK+VndzUiso+y7Ut24oTnXGtFY6i4rw0N5rfc/TUl9XpTd+hwnLnGuzHqtCbOWjZLzCMqq1pvWY93J66G+/qp3yIbJ3Lw+tc/jWQlJsQbkd/QpcTcSV3WHz64Xn3f0WS3XYIxD/2uQaqlhVbJbroGaL623vLONEllyjdbd4TQOzWcly4wEDs15jjHieV53xcnjomJxAkkxiHgdmPceYMTTZTY5DiYjfg2geBel6bWJD3+tb5hJFtnaY5j0VRSgQ30r0a2q50XjOnWrecpR5RxK0/NkZGkrQtM8aJMfnSt0LEdj3o3p3jh+da5Tn45Hz1u9l9QcVrk+o+R5NOxJvsct2/Gn+POzQOyznJ899R1GdSVkas71p8d26zGST8XarDFbJgejYGNLv4pN54bwWd9n6h9R5LkvzhX5nhax+PwRxbb1QqtXDAqkfOlzjNZhfI7ReozjY/5slwlPEW8dt/EZKO6slMmNaTZs5DFlX9ov5y7nY+rWHJhtzoPOnfEPZRSvzikq6zuPQOY/RtB5sDpz7LKvx88+8f737d73nn+M8uP5HfGZp4uj5nOeE7vWWGszZ/fIbzEaMqNvv2Q77indGvdcjn75HBPR0Kz1mx3L9Thtf3+vOA+J77fLn+MqWx7nPG7rrb2tw5Gc9deyJ9Vt0A7nepSX0ZrVHP+qrel7LLv8LlPys2nFto67dPx5exW9XcpIliVXMg7dS71HJLleqXL6tOLKRU5i6PSEJwW0DnKts65nfA523Xmw7igFZSm0OcFroVQZisvu0b60fOdrF+G5FeVV2dmyfZ62WrD7C3lhIyYw1+FyUhYiZ1U0g+deUmcZPdB/0pgO6F622Sv1omO9ztgv4ls6p55fLsLmTq6bU+Z+Pd47Ed9F5uVQXIfaTxn7B7PMet7PYuFTpd77rmSzIRu+EqP7RmTervZ7T285us7CwWXfTxTJqcfF0VhOH9JRyVPWR8nIjCK62LY1lq22Peua1MXa0ZfMkurNrd52eyaqYdmrj9c1mxviS9ENATedLGiFvvcYesOVFS4T0/hgP+a67DleM3fdYZ9y/B2TGWtsHMfj/fmsMR1bSXOfyutt2JY/j8u+82vYSs19a0v7nxRDsRkcXkprtuHzge3tcZLyOoYmvG+3W+nnmd6ysqF2j9cye/vJt+NcHzsbZk/8zLyMXrFo7N7WdbU7XstVXeeEWLvZHz9a6Uf4qFs2KjZ5gX2MNn+Qin04ld8+dxtm1eQhZHcLokQ3FBTRCLQVh5yZO/BFvRy4q++Hdm86XrD/JdSajEX8w8xijG5e23H7vA8ZxWf9uevUcj9ve6CK275lO+rmtOqaXIwoZtlJIIs2azRqdJtlvR5yTCHoespayErqd9u0zxhmJ6ncBrhub3qzHaPYCK576XG1WFIny/Ox0sXPLNie+Q2FHk0Pp8faCh2HlLJt4jaPNwTTvhVSXgrlY25Wl9ur3L1WdbXy5GZfv4lu7DRe/Xm83m+I9iZHcKONyXWN2SvBtMwzlT1uVeQ3d/rMEpv6VFc5tq7XfOjHdMW2xy1kra8fzx1/2g4d2xoX89N42q8LWHXRKH/Qn2JQApLueKCLBXKgH8h1oSnjjph00UwXzMbWkoN0cS906iLq+LFa49EjC3RBQD+ye3zhMrbVuKItxyrH8f/N+OG8EilCrl8giEfhNDbaK9EpE55aXO5zfOu629NBpx6tYUuy5qwLIXmBIn6o14WJvCDcLqRYRrZyGCqErjjlBQdNguLXRbI2d77YIdG+hc6wUP4VklaI4+57KbV5VVHt7nNdmutxpf9cHzEnEizwKtVYs7Qst6bcWTxDTZ3q1aqVN23Jo0rx6tbs6K0DlOZcfdnVQnal2mR7vflCjS7q6OLMYhwxvnviT4x1oSQ3B7w2JLYlsBSLdS6V9KMLl9FbF4HqolexXjJPhT37ZzVobHME01CqeWqQqBubXU9Id2NW41w2q0i85kfcLVttGlNeAG//ut/nhTLd1nPwXuupvy7+ymZd+NKFOh2rSqSoxDOt/R5kK7S5TwPVaZH99SyhxqkPqMbQ7aZytDXrkrfobC/LMad5rguZuvini9p1hwbVVS79mmPp1Bb7Vs62rKqv9bdgFvJTRLIx+sqi4k3t7iMaPIhmdq9equd8DWMdVlNt9rXCB7lqVDiz+CI8x7eKoMZWlmRTm0Wbq7Dplm0W0klfFgt56fh9rfSrTReLKzkn1l07H6fPkFfCQT0eR+9PWtt11w3Pn+QyuBFYNo0Xxz/9nuyYLGTdqOd4J0gZp8ZqGeu6Lp0uPzXa9novW7EtXtWm/9sF4RQIm7arfVqOffKL93OdEzMZJxOe6g4oelTTPXGsem7muxFWiEubYii76UflclJ7VSosBRbFVmnxS1Hn1dyHjX4noWzXeSY+U4QNxau51Z1/4uTbkpgj4UmJBk7Uaceq3w9qsB1S+kgm08siVolGeFo7+b4eCRK6OK87OMqv9mbSmcb7zSjX+cOOdE7Rn2LPzz95J8g6J2qtqs32ppCSW8eTLCsu2a14Z+lRTtQRf9+k2+oLe83I00/HowDjDps6FuoRUlGOc53nQ+uoxqZ5qISfBCSzYVBm/D5Y+/rcaP6VWFprRXbMqeKrgbmtONRxLM73P/iC+IxXwTcEoebx18Cqu3QyqUnn6Xyv1mfcAUIJRkrwSC01h6L81Z/mRokm2THaMsFESSY1R5nwpASW8JFr1L7ivJLnmpjjbi8TOPRZo95X8k6Jbfy+k1aNRcknEsuXKKicPVXOetmIkWdnMk/5HEbKp3Abbowqq5LWJkbJuDWYtw/DbLZwKqSalFrhyK4m4DTZZqbmucW0Mn2ob4wpYprDamOezaSNiEvJ1jp2M6kw5laPLrxfdw3LROzob59J6h9VtGMydARs/CMAvc/IidZKxWx+2udnhuRUx0d+F4oAsy/WR34ejfWo9kq4q8+Rc7yyrU1+nBiuMShurz2dP7JP6zRk63NV6TmeNBIvCkdtuV5icWU5YqhzUR3bOlbzOPdnu4yvfY8LzUzWiuPd2+wjvQpGuQ8RlT1DqomfeOp4iL8Y1333PpDJo9kb7boDmr7/aHOCl+deHORPj5994vFIeHrvP+6ejjs9ZXKr/uFK/OVkSDddj/dj2ajxdpGopxur9Hq1HnqdF5llPOCpT0U3W8wOVVeA2ua2atl8XXC2bqpPPjc1txpLx64ncwvh6j/bvmc419XCwrFKs3u2+Vqvx0xF3xp1KFVjdPT4POBmy+tq1Tw8rYxabtWcroZSlSxTa24eZPv8nALxov9f/qrXp4QX6drYsbqCOjSB23ojGA9olpOtlHCn97NQG/Kya9hdiLbKvIC3+m9H20V4bsVxVXa2bJ+nbSzY46zvlnhPGdsHUqxb47mu+E9dC3PM1xXrHMOdLF/HeC8yL3eSyQeDr+uY95nrnVoDev8933v/HOVp5ascy3XPi0dccah2+H30OmPd952fFnOu/ZnrtHkvPY+7xrtuu8ga2rcxfJxRWqhWRa9bM7HVbvVZXrwuuo21IAu2WvZsdus4O3UetnQd69J3zYPtWubwXrEeGvfc5zHNlqynHwHG/Pszuvfzl9PeNpvZ9D8M2otVZhvrOZvjSHnBX8HwSGa72eaOmL8tu/bvvefV9bFvlrWzzS6shtnz0DpW6updaGljPcytulWXjOJrbQhnVLFXwlOOfWnequfebyY8rWz3qoKetvzC3Zpqt+xP0VknuvclND4bVaFNyGh0Z5nbsLAQ2KrYbPcTQgW02VwqtQj2PdnOUnzUIma7sI3ReVrJ+h3KtBh8XHntq+7yIeu2txXPoT63y6b16kefeM1fq8vbGG3UQ2l1GJeQumzQxtSz1dY1ht8uavmuGD2Tw4xP9ptcj01z0gOYHJxRtL2zdOVua1gyP7cr1qxPMQtMhdYHt4hqv3VuWVgPPfdVu17dsjB6oCL5eT2dorv2UZ7TUB972Ymx987oj8aan2LivpTtfcWv1nwo5/8hHz9
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
1ZfLdnGYQ/a5Pn9wSnqa8qVnuEuVyPt/IaZGLteiIcFLcOtzh6RLgUYUABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgMCVEfjwR17Rb86URuc7NLWezD0aLyvfLTPphnODdJOnka2kvKFRK1UnN6nmvtKOOglPK75UIQABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAIFO4OIJT0pRcrpS7KeEJ7Ur58n3TLKUnc4JT2pT/12V8JQBZkQV1q0YyWOPvtXxs4cABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEronAKY+0U2hOYFomL90YiUo3awCV5DRL3VL602J0PeHJWU5T742Xt0faTW1XVHTy0mxuisDpWdkdIWaXdUh4mqlRhgAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAwHUROE/CU96LaZEXNCUyTUWlOM3bsqa7OU15RhKcqlPCU6n58Xi6y9J687PztvrWsvayNDPZVEc6i32US676ucPTPk1aIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgcB0ERsJTPYZufsDcnLhUeUctdannBkW9EoNKrQ+g7vzkRCdnFY16tSzylZrQlPDUrV1RwclLszmHFm0eSHaT8DRTogwBCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAIG7hcBIeGoR+Y5KWXWKUrv3Ud65KXKElCaUcquEJ4vfcmF7lL7Dkx9157qkSXjaZkYrBCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIBAEzpfw1JBtJTzdnHEqEWquL8tOcPqASXhS+G//tbcsR0ENAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACELjjBE5KeIqnvd3IhKZFVlM+BK7fy+lmL40xTElPTnIanVNpkrvxya96fVY3zE0aFymWl8WT6+a0rEVHhJvi8aJbWUWFhKeLMEcHAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACV0vgwglPkQMUaVD5kLuM6EjC09FkJylXKlKaud5H2uVz+lo0LeHplgYaf489+tYMkBcIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhA4PoIPPzSV04JR5Hkkzk/jmfcZmnR3FKCQtiCu1tRHLUbcw5TyJSC06OcAHXDRru9sPHydoenbvnKCuUlcpembaqog4SniQ1FCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEI3H0EPiISnkYOkHN+HOeUwjSKzl8KodGohCdttTsx4UnSNtFSj/YSntRuGTlY19W2tTV7vcshjcGqa5JadOiuTqOfOzx1jBQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCBwrQTWj7S7FTc5cn6RU4B0J6a6GZN6IhFIuUDZEPUm5ISnGkzZGNlEVbJl3+FpL3kpxM5MeDoPLQdQA6qaB1V2LBG1RQcJT+fhjCwEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAATuFIFjCU/zrZVGwlNEptygVcKTsqScPTQSmzwK97je9mquZKTesZfw1Hs2Cn4mnu7ApC3tRWCy6bbsyJcmU7vW3HOvclAjFhKeBjdKEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQuHsIrBOe2q2cWoAjA0j5TXMiU+UTRYvzi3q6k1T9/DiPsyUZLXKNUqwE1N5c7SU8ycF+8pINL/eTnWVH1sr74kZOEXSPKTrGcEl42gBIEwQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABK6dwHkTnpwf1BOeNAIlCunPnVFx0TlEmVnkRuloG51VV9PLX/X6hdic8OQO63WtVuh3nlp3ZL20SXjahEMjBCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEPiAIzAlP9YQ4JSstc4PyLk6ZZDQyjRYJTxrpzTbcVK2EpyFdfXs3arJA6pTPvYSnZvbobk6KkuC6XsrLQc1tWV5kQnGHp+LDKwQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABO4uAnPCU0amZ9f1bZSruep6raSo0d9VsnAg4aklUunReE6qSvGW8JQ3aFrf4Ul9h9zY6ZzgpLK2veyqVRZX6VYSVFOopqqEvgrVL1uPPfrWqZ8iBCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIHAdBNYJT7ciu2jkPI1Mo0MJT0pe0rZIYMqEpv08Jcvs6URakfOTbnxyPNJuuL08EqUsOcQMtHKYmuGpwh2eLg8bCxCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACELjNBEbCk7KCIv9nZDtFfWQezQlPFVKkL0Vmkm+o1G+GlFpDz0lOpXPgNdyS8HSADc0QgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAwCBwLOFJCU3alOy0l/AUN0Squ0E5ualullSvvoeT0qCqZXjcKKWIEqjCz/qRdhviF2yqQBY3cpqDS+8aTMhpcC0oOeORdhdEjhoEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAASumMBIeGqGT7nDU88NavlBoVrZRA5uTnia7xNV/U6CKinnIdX++hOeMtOJhCdPJXsIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQjcTQRuR8LTSHeqRCjfA8rj3kx4UmfkGl0q4UnP1/Oz8exs7J1ZNVoWeVp1S6fWOSU8KQMs+t7+a2+ZFSlDAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAtdAYE54Ur6Q7sfkhKRxb6ZIRMqspZs9wkpiqvyizCSqhuWj76JDfa1rugtU5R51Y1Ph7kt4asGR8DTNEkUIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhcE4E54Ukh3LhxcyPhSe3Zu4pSje1mSFEsmaWc7pvkhCf1VKrTbUp4koPDm51WOHUnKLeF1nSHp8z4yq7ql+xjj771sGl6IAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAIE7QmCd8DRlLYX/kaqUyUxK/2lNlR4UD6+ziPeZIhQV10PFT5nzo+58B6len7KiLnWHp+PEnLw0S1VbtkwJTwr+1vPqsw4JTzM1yhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACELguAh/x0lf2hKSMoWcwqTaylqpZ9SlHyP1qGqJVafVKdpo7Z32pua/aDyY87fkIN+fbysGc17QYTHR0H4opKrduPZ8uuMPT+UgjDQEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAgdtFQAlP2nwXJt2yyTlBN6bkp5HwlNKpk1lOlUa0nfCknKGRRSQvTa/tVI3cokp6qr6DCU9LzYvUyoEHVxamgBYdEXZ2WYc7PF2EODoQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhC4agLneaSdcoCmHKjIX/LdmSKqKDp7aNy1aRltJT8t21yzpStLeBrB2IWTl1zX3lIqTuVoJ+Fp5kQZAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACdweBCyc8ZXqQ05TWuU+jfR7lsYQnyUnryhKeZsdVPjvhSRIVOglP+/xogQAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAwPUTeFiPtKtUoAxGj7EbiUkjcWn5SLsQjTsg3WoPo5Oib/aUcq6oXZ19W9ayc7hIKRKeOiwKEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgMCawIUTnsJQPuKuGVSOU+Yu+aXlNq1SnKQ1QlDxbkp4miPjkXaDBiUIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQjcLQQefuSVLZSWiFS3cmptIxtp7w5PKTH6eyJTCqo97DWTzVjf+Q5SNyLbyWV3XusdnhyEIifhadCgBAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAgbuFQCU8TZlJV5Lw1EY3mT023jnp6e5IeHLC1q3nM+5bkf302KNvPTYG+iAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIACBO0Dg4UdeMXmJRJ9MeHKm0riDU29uCVHKAdLz6LpEK5Tm1D5Z3y+WDbU76Wkv4elGOCxn++rna6nQ6s5N1qy2rC06uMOTCbGHAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIDA3URgnfCk/KIp/aiHWnlOPb0pREayUgpF18geioSnMtP1twvWGD6PJjzN4jJYwSoW92y7qdaSWYpOeosOEp6OkaQPAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAAC10VgTniq/KGRqXTrVtxcKQJT8lL+tfs5jfwiydYWotM2Ep5altHUN+4L5bs6yUklVMX+5a96/ZSFtNC7ZKWFsrA+VXoGV7TFYG49r77q14B5pN0l8aMOAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIACBKyAwJzylOWceVaV7qOaW1dRvhjRlOSlHaEj3R921jKGsu3/7gXeV/kTCU4dIAQIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhBYE7h0wtOU8yTbSmqaE5qq7rSn8j73V4v0SubMhKcyaLXz7FsQtWuKU6VncamLR9qdhyyyEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQuFMEHn7kleFqyvu54B2e/Ei7yn8aWVCy7JqTmjS2Oelp0X7WI+1mgzJ0+laDXOQ1zQNfdJDwdDpXJCEAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIHDnCFTCk/y1pKcp4ckpQDei7axH2p034UkeK+lpTneKtrMSnqR4sY2Ep4txQwsCEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAJ3D4FjCU/j3kxRyts0tXs1ORNK927y7ZumrvnuTRppS6XqonOKk9W7DAlPd8/iIBIIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQjcbQSuNOEpspaUuLROeNKYq71Gv5Xw1GU++VWvz+Qn3VYqt55dVdWLv6bZ3dJcte37qUfaKYRboaC/xx5968VdowkBCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIXAmBhx95Rdmp5B7fyqnZ9v2X3Fx15f9UPtKNzAWSsNOTmrHFrt/iqVqjWnlG8yPtnCQ1HmnngJrS5XckPF2eIRYgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCBwvQTOn/Dkmx8p+aklROmuSSM3arRraGqf76M0VZzkNCdAjYQnKV/B5jtF3br1fFpTrGNrSVDRcCMH0aKNcsm1/qhwh6dBjRIEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAASui0BPeHIAi1s1jSymbFb6T29SoVeUMNQsVLvuAlVb3A2qyTmxKXOeQixzkUJsyEbby9sj7Zr2Fe4qoB5XWnaQUVl0kPB0heAxBQEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAgSsj8PAjrwxbU97PsYSnOcFJETS13CmBqUcVj7rr5Y2Ep943dJxudK0JTw5a4VdA1aKMLO7wNM0aRQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCFwTgUp4kvOW7XPOhCfnCDnbqZKeTkx4CmW720t4kuGRQXUVdCpUOyqLPXzu8HQViLEBAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIACB20zgXI+028tAiowkJxAtkpMWFadS9ZGse9NEa+x3eCLhqfOiAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQg0Ated8BTPjus3l
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1QurpDI/pQQ88eShCa01XDSVbLJdsH20rByVMOCJ4azy8t+tkaIPwJOlGF6hQLIKAHhKVjH/7Bnoear/ffTbvgNyUj5erbhdI6pUvTKtmLOY9u/Zrxdre3xnGjLefQyg7syzwrJZi2nRf+ZR2efLaN/ufXp3F8+4jmqW1NL1eAX18BrAUzyV0Bc0BQA8BS1i8DeRAgCeEimE/iAqAOApiFGDz4kUAPCUSCH0B1EBdc8I4CmI0YPPsRQA8BRLGbQHWQEAT0GOHnyPpgCAp2iqoC3oCgB4CnoE4X80BQA8RVMltTYNPCneJ+6RdtYaFhfFhJGDMlJFOY+quH3SUJPdHJqHM0FZ7FFKwJN7mVg1awG1T8sKwFMstdAOBfxSAMCTX0omPw9ncZp+zUu04pMlcQfXqFeDLnzvWqpYpWJcu3zp/OefHqPNyza4ttN+cDc66d6hrrZ4FfXwGsBTPJXQFzQFADwFLWLwN5ECAJ4SKYT+ICoA4CmIUYPPiRQA8JRIIfQHUQF1zwjgKYjRg8+xFADwFEsZtAdZAQBPQY4efI+mAICnaKqgLegKAHgKegThfzQFADxFUyW1tuK2/eyBNvsTF3gKA5l4iGrid1G3ZrEaVZfbM4U2WfY6EZMa2WXQNbYn7mHp16xpvQJPONIufcUxAxRgBQA85f5z8P2rc+mzSe/Tnu17XM4UVSiibmf2on6jT6QKlSu4+vK5Eg48NevRiv709AWhf9A8bF49vAbw5EEsmARGAQBPgQkVHPWoAIAnj0LBLFAKAHgKVLjgrEcFADx5FApmgVJA3TMCeApU2OBsAgUAPCUQCN2BVADAUyDDBqfjKADgKY446AqsAgCeAhs6OB5HAQBPccRJsssCnhyIkVfgSWVyCqOaEgNPoWxOknES44vE/xQGpTM8WemfIncTqz3SMrwlCeCJ3ZHmagyOtAtXE3Uo4FUBAE9elcq83W97f6M1X6+kfXv2Uckhjal20zqZX9TAFdbOK6OVn/1EFStVoNLebahRl6ZJe6keXgN4Slo6DDBYAQBPBgcHrqWkAICnlGTDIMMVAPBkeIDgXkoKAHhKSTYMMlwBdc8I4MnwQMG9pBQA8JSUXDAOiAIAngISKLjpWQEAT56lgmGAFADwFKBgwVXPCgB48ixVQsPitn2lDZM9kl1yAE8WW2QRTVazopuEtfy/qouxqijey8utinzlFwsbCkFNDsDJ5aCwA/DkUgQVKBB8BQA8BT+G2EGkAurhNYCnSG3QElwFADwFN3bwPLoCAJ6i64LWYCsA4CnY8YP30RUA8BRdF7QGWwF1zwjgKdhxhPduBQA8ufVALT8UAPCUH3HELkIKAHgKaYFS/igA4Cl/YomdhBQA8BTSIt2SAp70PJpc4hYLXJIlTS9xTSVACu9XQ0TGJhtyktNFAZ7kHDyVupS9CUfayXRT0iHLK/6yZOHMScpVvEMBKJCEAgCekhALpoFRQD28BvAUmJDBUQ8KAHjyIBJMAqUAgKdAhQvOelQAwJNHoWAWKAUAPAUqXHDWowLqnhHAk0fBYBYIBQA8BSJMcDJJBQA8JSkYzI1XAMCT8SGCgykoAOApBdEwxHgFADz5FyIFPBUJMom/EwilauI1woGmUF3ain7dogryPQQ8hTwt19b6+DrR6cCKpKnO8BQa6FdJwUvO+aw22aIQLVEB8OTUCGUokJ4CAJ7S0w+jzVRAPbwG8GRmfOBVagoAeEpNN4wyVwEAT+bGBp6lrgCAp9S1w0hzFQDwZG5s4FnqCqh7RgBPqWuIkeYpAODJvJjAo/QVAPCUvoaYwSwFADyZFQ94448CAJ780RGzmKUAgCf/4pEq8GRleYoBPIkj7Rwkke0stzANFerRtVBT6Eg7L1tkSosvi75KNMJaxcE18UjpjpzF1SHapbkagwxPidRFPxSIpQCAp1jKoD3ICqiH1wCeghxF+B6uAICncEVQD7oCAJ6CHkH4H00BAE/RVEFb0BUA8BT0CML/aAqoe0YAT9HUQVtQFQDwFNTIwe94CgB4iqcO+oKoAICnIEYNPidSAMBTIoXQH0QFADz5FzUFPOkZPR1px9bMAlm8kR6rC3a7/aZRIlWIMkw1JZ3hyTv0pOAl7aUoWG2yRTlnVQA8OWVCGQqkoQCApzTEw1BjFVAPrwE8GRsiOJaCAgCeUhANQ4xWAMCT0eGBcykqAOApReEwzGgFADwZHR44l6IC6p4RwFOKAmKYkQoAeDIyLHAqTQUAPKUpIIYbpwCAJ+NCAod8UADAkw8iYgrjFADw5F9IFPCU7JF2ER4wsaTxIVFRBBM36/ZQszrWLnyepIEnnsAb9JQa8CRHiR0snDkp3FfUoQAU8KAAgCcPIsEkcAqoh9cAngIXOjgcRwEAT3HEQVcgFQDwFMiwwekECgB4SiAQugOpAICnQIYNTidQQN0zAnhKIBS6A6UAgKdAhQvOelQAwJNHoWAWGAUAPAUmVHA0CQUAPCUhFkwDowCAJ/9ClRzwxOs6SCZRVqfJFVWwfZKAUAh4smAnizVyHIAnjTX0ZHXLtpSAJ3vpBG/WKpq+4mqRY2XdwdPgSLsEYqIbCnhWAMCTZ6lgGCAF1MNrAE8BChpcTagAgKeEEsEgYAoAeApYwOCuJwUAPHmSCUYBUwDAU8ACBnc9KaDuGQE8eZILRgFRAMBTQAIFN5NSAMBTUnLBOAAKAHgKQJDgYtIKAHhKWjIMCIACAJ78C5ICnvSMUY60k4hT6EWYSlhIvDuAJ+6XNjyTLmggiludl4KfNPQkOuUUXQZdIykkhSKFpgoN95bRKWRvlawZNdck96BWERa6g60BPFma4RUKpK8AgKf0NcQM5imgHl4DeDIvNvAodQUAPKWuHUaaqQCAJzPjAq/SUwDAU3r6YbSZCgB4MjMu8Co9BdQ9I4Cn9HTEaLMUAPBkVjzgjT8KAHjyR0fMYo4CAJ7MiQU88U8BAE/+aYmZzFEAwJN/sVDAk8R/eNoYwBM3l2uQSVmHAU9yvH7hggU8KaxIwUt2XR6jJ+GpECJV1HXwaPHdhBrBi4Y65YweXvT5fC5ba07H1KI3tA6AJ5dYga2UlZXR0qVLqVGjRtS+fXuqXLmyb3vZuHEjrVixgnbu3EmdOnWSa6Qz+ebNm6l69erEwITXi7/g4P2tXbuWWrduTS1bttRHOsabg31euHAh7dixg9q2bUstWrSIZ+5rH4AnX+XEZIYooB5eA3gyJCBwwxcFADz5IiMmMUgBAE8GBQOu+KYAgCffpMREBikA4MmgYMAV3xRQ94wAnnyTFBMZoACAJwOCABd8VwDAk++SYsIcKwDgKccBwPIZUQDAU0ZkxaQ5VgDAk38BqN+unzsLUxTgiVezmm1iiaEh2aAIJraw2SHZ5GgXzSqLU7SsTjzSeUngiRsU9MTTOqZz2sYsA3iKKU1edjDM88ADD9D69esj9le1alW6+OKLqVu3bhF9Xhvmz59PU6dOlbCQc0yFChWoY8eONHLkSE/QEY/lG6h3332XPvvsMznfmWeeSf3793dOG7W8bNkyeuyxx2jXrl0R/fXq1aPrrruOiouLI/q44ZlnnqEvv/zS1VezZk26+uqrqbS01NWeiQoDT3PenU1/7H4SHX58D2rYqUkmlsGcUCCrCqiH1wCesio7FsuwAgCeMiwwps+6AgCesi45FsyCAgCesiAylsi6AgCesi45FsyCAuqeEcBTFsTGEllTAMBT1qTGQllUAMBTFsXGUllRAMBTVmTGIllWAMBTlgXHcllRAMCTfzJnFHiyGaikgCd1pJ1/W1QzWd54yvAkCKvy39lejSmnhTMnqYnwbpACK1eupPHjxxP/UIh3DRo0iE4//fR4JlH7eP57771XA3jRjBg4uuWWW4gholjX559/TjNmzKA1a9a4TLwAT7Nnz6aXXnrJNS68UrFiRbriiiuoa9eurq4XX3yR5syZ42pTlSpVqtDDDz9MlSpVUk0ZeWfg6YuXP6HSn+vJ+StULKK6LUuofodG1ED8YQCq5JDGVLVW1Yysj0mhQCYUUA+vATxlQl3MmSsFADzlSnmsmykFADxlSlnMm0sFADzlUn2snSkFADxlSlnMm0sF1D0jgKdcRgFr+60AgCe/FcV8JigA4Mm
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
1IYDwFJZMEEeQBBCegqTJWGEgkKnCk2JfuWy9PHvW/8WUnvYZM1iOmTRWBOcpDJdqSjEgPKWEi86GEEB4Ci5RCE8IT8FdTYwEAQ0BhCcNFKqMJ+A+vEZ4Mj6VTMBDAOHJA4NdKwggPFmRRibhI4Dw5ANC0QoCCE9WpJFJ+Ai494wITz4wFI0mgPBkdPoIPgYBhKcYYKg2lkAmC08qaZVL18szZz4k1ZuqtTkceMJQGX3zD5GetHTCW4nwFN7cEFn6BBCe0mfnPxLhCeHJf01QhkCgBBCeAsXJYCEh4D68RngKSUIIIxACCE+BYGSQEBFAeApRMgglMAIIT4GhZKAQEUB4ClEyCCUwAu49I8JTYEgZKAQEEJ5CkARCCJwAwlPgSBmwiQlkuvCk8K9fuFamnT0lvvR0S0R6YjOGAMKTMaki0BQIIDylACtBV4QnhKcElwjNEKgfAYSn+vHj6HAScB9eIzyFMz9ElR4BhKf0uHFUeAkgPIU3N0SWPgGEp/TZcWR4CSA8hTc3RJY+AfeeEeEpfYYcGT4CCE/hywkR1Z8AwlP9GTJCuAggPH2bDyU9PXPWQ7Kjaoc2QYPGHSRHXH+cto3K8BFAeApfToio/gQQnurP0B0B4Qnhyb0W+IRAgxBAeGoQrAzaxATch9cIT02cCE4fKAGEp0BxMlgICCA8hSAJhBA4AYSnwJEyYAgIIDyFIAmEEDgB954R4SlwtAzYhAQQnpoQPqduMAIITw2GloGbiADC0//AV8xbLdPPeTim9DRswig59LIf/O8A9kJLAOEptKkhsHoQQHiqBzzfoQhPCE++S4IiBIIlgPAULE9GCwcB9+E1wlM48kEUwRBAeAqGI6OEhwDCU3hyQSTBEUB4Co4lI4WHAMJTeHJBJMERcO8ZEZ6CY8pITU8A4anpc0AEwRNAeAqeKSM2LQGEp7r8lfQ0bcIU2bltZ92G/5YOufRIOejsQ7VtVIaHAMJTeHJBJMERQHgKjiXCE8JTcFcTI0FAQwDhSQOFKuMJuA+vEZ6MTyUT8BBAePLAYNcKAghPVqSRSfgIIDz5gFC0ggDCkxVpZBI+Au49I8KTDwxFowkgPBmdPoKPQQDhKQYYqo0lgPAUnbrVs1dGVnqaIru210Q3Rmq+e/XRMvQnI7VtVIaDAMJTOPJAFMESQHgKjifCE8JTcFcTI0FAQwDhSQOFKuMJuA+vEZ6MTyUT8BBAePLAYNcKAghPVqSRSfgIIDz5gFC0ggDCkxVpZBI+Au49I8KTDwxFowkgPBmdPoKPQQDhKQYYqo0lgPCkT93Kz8vlufMekZqdu7UdjrzlJNn3hCHaNiqbngDCU9PngAiCJ4DwFBxThCeEp+CuJkaCgIYAwpMGClXGE3AfXiM8GZ9KJuAhgPDkgcGuFQQQnqxII5PwEUB48gGhaAUBhCcr0sgkfATce0aEJx8YikYTQHgyOn0EH4MAwlMMMFQbSwDhKXbqyj9aIs9f+Kjsqdmj7TRm0jjpf/QgbRuVTUsA4alp+XP2hiGA8BQcV4QnhKfgriZGgoCGAMKTBgpVxhNwH14jPBmfSibgIYDw5IHBrhUEEJ6sSCOT8BFAePIBoWgFAYQnK9LIJHwE3HtGhCcfGIpGE0B4Mjp9BB+DAMJTDDBUG0sA4Sl+6ha+PU/+8fMnZe+eyJfDvi0rK0uOueNk6fuDgb4Wik1NAOGpqTPA+RuCAMJTcFQRnhCegruaGAkCGgIITxooVBlPwH14jfBkfCqZgIcAwpMHBrtWEEB4siKNTMJHAOHJB4SiFQQQnqxII5PwEXDvGRGefGAoGk0A4cno9BF8DAIITzHAUG0sAYSnxKn75o058vJVT2s7ZmVnyUl/OUvKhvfUtlPZNAQQnpqGO2dtWAIIT8HxRXhCeAruamIkCGgIIDxpoFBlPAH34TXCk/GpZAIeAghPHhjsWkEA4cmKNDIJHwGEJx8QilYQQHiyIo1MwkfAvWdEePKBoWg0AYQno9NH8DEIIDzFAEO1sQQQnpJL3ey/fyav3/i8tnNufo6MnXKOdBrURdtOZeMTQHhqfOacseEJIDwFxxjhCeEpuKuJkSCgIYDwpIFClfEE3IfXCE/Gp5IJeAggPHlgsGsFAYQnK9LIJHwEEJ58QChaQQDhyYo0MgkfAfeeEeHJB4ai0QQQnoxOH8HHIIDwFAMM1cYSQHhKPnVfPDtLZvz2Je0B+S3y5eRHz5N2fTpo26lsXAIIT43Lm7M1DgGEp+A4IzwhPAV3NTESBDQEEJ40UKgynoD78BrhyfhUMgEPAYQnDwx2rSCA8GRFGpmEjwDCkw8IRSsIIDxZkUYm4SPg3jMiPPnAUDSaAMKT0ekj+BgEEJ5igKHaWAIIT6ml7sOH3pb3752hPaigqEBOefx8adOtrbadysYjgPDUeKw5U+MRQHgKjjXCE8JTcFcTI0FAQwDhSQOFKuMJuA+vEZ6MTyUT8BBAePLAYNcKAghPVqSRSfgIIDz5gFC0ggDCkxVpZBI+Au49I8KTDwxFowkgPBmdPoKPQQDhKQYYqo0lgPCUeupmTHpFvnj6Q+2BLdu1klOfOF9admytbaeycQggPDUOZ87SuAQQnoLjjfCE8BTc1cRIENAQQHjSQKHKeALuw2uEJ+NTyQQ8BBCePDDYtYIAwpMVaWQSPgIITz4gFK0ggPBkRRqZhI+Ae8+I8OQDQ9FoAghPRqeP4GMQQHiKAYZqYwkgPKWXuld/+ZzMfeVz7cFFXdrKqZGVngqKC7TtVDY8AYSnhmfMGRqfAMJTcMwRnhCegruaGAkCGgIITxooVBlPwH14jfBkfCqZgIcAwpMHBrtWEEB4siKNTMJHAOHJB4SiFQQQnqxII5PwEXDvGRGefGAoGk0A4cno9BF8DAIITzHAUG0sAYSn9FK3d89eeemKp2XRv77WDlDSu2NEejpP8grytO1UNiwBhKeG5cvoTUMA4Sk47ghPCE/BXU2MBAENAYQnDRSqjCfgPrxGeDI+lUzAQwDhyQODXSsIIDxZkUYm4SOA8OQDQtEKAghPVqSRSfgIuPeMCE8+MBSNJoDwZHT6CD4GAYSnGGCoNpYAwlP6qdtbs0eeu/AxKf9okXaQzkN7yNi/nCk
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
1wVFJAvrwE8qeAt2OivAgCe/FUK81RRAMCTKp6CnYEoAOApELUwVxUFADyp4inYGYgC8pkRwFMgqmGu0xUA8OR0D8G+YBQA8BSMaljjZAUAPDnXOwxX/K/bR3T4578sjYwqkJ1e/Kg9pc3k+btKy8nJrBPAUzJz+B0+7n83/6OdX22jDbPW0IVj531eLVWalFSmcRWq1v6RsEKJAJ58Su/3BABPAJ78vlmcOHHIkCF0+vRp3TSOkFSqVCk6deoUbdy4kfibhSwTJ06kTJluh4TkyEXDhw8XwylTpiSGkzjikbFwSjpOTSdLvnz5BBTE8BGDTHv37pVDIt0cp50zFo7y1LdvX+JfuHHhdZxKLk+ePPTXX395rLeCivgfcYaR+Bcbcn2FChWoUKFCIqrTtm3b9L05IsekSZPEPKsvMpIUn4F1MxYGu1grjvzEKQBLlChBS5YsodWrV4tpDzzwALVp08a4xO86gCe/pcJEhRSQL68BPCnkNJjqUwEATz4lwgTFFADwpJjDYK5fCgB48ksmTFJMAQBPijkM5vqlgHxmBPDkl1yYpIgCAJ4UcRTMDEgBAE8ByYXJCigA4MnZTvr32r/0abv5dGLb35aGZi+eh5rMax1SmizLjRXvBPCkuAMdYj6DTru++YM2vLOGYo+e82lVylQpqNRTFenBTrXvSMpJAE8+XeD3BABPAJ78vlmcOJGhJYaM+IHbW2nSpAk9+uijHlOM6e54gEGfcuXKeczhX5RxVKVz57x/42NQqH///lSgQAGP9dzgCFIMTkloKdEErYOjS3E0Kk7NZy4cRWru3Lugw/AAAEAASURBVLk62GQe5zZfv0+fPhQdHW01rPetWLFCRJCKiorS+7iyfPlyWrp0qUefbDCkxWnzMmbMKLsC+gTwFJBcmKyIAvLlNYAnRRwGM/1SAMCTXzJhkkIKAHhSyFkw1W8FADz5LRUmKqQAgCeFnAVT/VZAPjMCePJbMkxUQAEATwo4CSYGrACAp4AlwwKHKwDgyeEO0sy7Hn+dlrSZR6d3HrM0NmfpvPT8nFaUJr1ngAbLycmkE8BTMnH0nTqmxoLs/m47/fbOjxRz+KzPq6RIkYJKPF6OqnetQ/fcm8Xn/GAnAHgKVrnE6wA8AXhKfFco1sMvkTh605EjRxJBQRz1iFO0ceQnczl//ryAjPiXBgwcTZ48mTjSk1X53//+J6AgM7TE3/SKFy8uYClzSjzjPmfOnBHRl8zgFK/niFQMWzG0ZFeOHj0qIlBxCj5j4fU5cuQQUaQyZ85sHAq4vnjxYvrhhx881jHM0bVrVypatKhHfyANAE+BqIW5qiggX14DeFLFY7DTHwUAPPmjEuaopACAJ5W8BVv9VQDAk79KYZ5KCgB4UslbsNVfBeQzI4AnfxXDPBUUAPCkgpdgY6AKAHgKVDHMd7oCAJ6c7qEE+65dvkYfN3+Xzh+4ncHGaHme8gXouXdbUqqIVMbuZFsH8JRsXR/awTUGZM/yP+m3mT/S+UNn/NqrWP2yVL1zbYoqkM2v+aFMAvAUinqeawE8AXjyvCMUb3EqO04zx5GSihUrZgswyWPyL8IYfMqWzb9vXAwccSo6fnHFoBPDRoEUvh6vZzvZPk5jF0jh9QcPHhRwV5EiRYjT04Wz8DfXPXv20NmzZ4n3t4pYFej1ADwFqhjmq6CAfHkN4EkFb8FGfxUA8OSvUpinigIAnlTxFOwMRAEAT4GohbmqKADgSRVPwc5AFJDPjACeAlENc52uAIAnp3sI9gWjAICnYFTDGicrAODJyd7xtC0+Np4Wv/IexfxtHXGmQLWi1Gjay5QitXWgBs/d3N0C8ORu/4b9dBJ0mrXGFio0X7No3fsE6JS1cHbz0B1rA3gKn7QAngA8he9uwk5QwEIBAE8WoqBLeQXky2sAT8q7EgcwKADgySAGqq5QAMCTK9yIQ5gUAPBkEgRNVygA4MkVbsQhTArIZ0YATyZh0FRaAQBPSrsPxtsoAODJRhh0K6sAgCe1XHfl/BX6RIv0dOHYeUvDC9csQU9NejHZQ08AnixvD3SaFdCYj4TUdWu01HX+RXQqUqc0PdTtUbqboJM0G8CTVCL0TwBPAJ5Cv4uwAxTwogCAJy/iYEhZBeTLawBPyroQhlsoAODJQhR0Ka0AgCel3QfjbRQA8GQjDLqVVgDAk9Lug/E2CshnRgBPNgKhW0kFADwp6TYY7UMBAE8+BMKwcgoAeFLOZXTpdBx9/NIsunQ2ztJ4AT1NbkopUqWwHE8OnQCekoOXgz/jfzf/o13f/EG/z15rGzHNvHt07VL0UNc6lK1ITvPQXWsDeAqf1ACeADyF727CTlDAQgEATxaioEt5BeTLawBPyrsSBzAoAODJIAaqrlAAwJMr3IhDmBQA8GQSBE1XKADgyRVuxCFMCshnRgBPJmHQVFoBAE9Kuw/G2ygA4MlGGHQrqwCAJzVdF3skRktv9y5diblseYAij5ahBhOepxQpkyf0BODJ8rZI9p0MOu38ehv9PmstxR4955cehWuVpOoa6JSjeC6/5t/JSQCewqcugCcAT+G7m7ATFLBQAMCThSjoUl4B+fIawJPyrsQBDAoAeDKIgaorFADw5Ao34hAmBQA8mQRB0xUKAHhyhRtxCJMC8pkRwJNJGDSVVgDAk9Lug/E2CgB4shEG3coqAOBJWddpKbjOCegp/kK85SGK1y9Hj7/xbLKEngA8Wd4SybaTQacdX2yl399da5sO0ixOoRol6KHujzoCdJK2AXiSSoT+CeAJwFPodxF2gAJeFADw5EUcDCmrgHx5DeBJWRfCcAsFADxZiIIupRUA8KS0+2C8jQIAnmyEQbfSCgB4Utp9MN5GAfnMCODJRiB0K6kAgCcl3QajfSgA4MmHQBhWTgEAT8q5zMPgc/vP0Ket55A36OmJMY2JklmgJwBPHrdJsm3cvH6T/vzfZto4bz1dPB7jlw6cErJ6N2eBTtJwAE9SidA/ATwBeAr9LsIOUMCLAgCevIiDIWUVkC+vATwp60IYbqEAgCcLUdCltAIAnpR2H4y3UQDAk40w6FZaAQBPSrsPxtsoIJ8ZATzZCIRuJRUA8KSk22C0DwUAPPkQCMPKKQDgSTmXJTKYoafFLd6lq5euJhrjjlINKtBjoxolK+gJwJPlrZBsOm9cvUF/LNlIm+avo8tnL/l17uhHStFD3epQtiI5/ZqfFJMAPIVPdQBPAJ7CdzdhJyhgoQCAJwtR0KW8AvLlNYAn5V2JAxgUAPBkEANVVygA4MkVbsQhTAoAeDIJgqYrFADw5Ao34hAmBeQzI4AnkzBoKq0AgCel3QfjbRQA8GQjDLqVVQDAk7Ku8zD89J6T9GmrOXTtyjWPftlg6KneyGeSTXo7AE/S88nr8/qV67Rl4S+0ZcEvFB97xa/DF6lTWgOdHqWshbP7NT8pJwF4Cp/6AJ4APIXvbsJOUMBCAQBPFqKgS3kF5MtrAE/KuxIHMCgA4MkgBqquUADAkyvciEOYFADwZBIETVcoAODJFW7EIUwKyGdGAE8mYdBUWgEAT0q7D8bbKADgyUYYdCurAIAnZV2XyPCTO47TZ+3m2UJPxR4rS5zeLkVK9+e3A/CU6PZwdcfVuKu06cOfaNvCX20jnZkFKFr3PqreubYSoJO0HcCTVCL0TwBPAJ5Cv4uwAxTwogCAJy/iYEhZBeTLawBPyroQhlsoAODJQhR0Ka0AgCel3QfjbRQA8GQjDLqVVgDAk9Lug/E2CshnRgBPNgKhW0kFADwp6TYY7UMBAE8+BMKwcgoAeFLOZV4NZuhpaZs5dP2fG5bzBPT0hgY9pXI39ATgydL9ruu8cv4KbZy/nrYv2UDX46/7PF+KFCmoaD0NdOpSm6IKZPM532kTADyFzyMAngA8he9uwk5QwEIBAE8WoqBLeQXky2sAT8q7EgcwKADgySAGqq5QAMCTK9yIQ5gUAPBkEgRNVygA4MkVbsQhTArIZ0YATyZh0FRaAQBPSrsPxtsoAODJRhh0K6sAgCdlXWdr+PFtR+l/nd63jfQUXbsUNZz4gquhJwBPtreHKwYunY6jDXPW0Y7PNtK/1//1eSaOasawX/XOdShL/iif8506AcBT+DwD4AnAU/juJuwEBSwUAPBkIQq6lFdAvrwG8KS8K3EAgwIAngxioOoKBQA8ucKNOIRJAQBPJkHQdIUCAJ5c4UYcwqSAfGYE8GQSBk2lFQDwpLT7YLyNAgCebIRBt7IKAHhS1nVeDT+1U4v01NY+vV2hGiXo6ckvUorUKb3uo+oggCdVPefd7pi/z9GG2Wtpz3d/0M0bN71P1kZTpUlJJZ6oQNU6PEz33JvF53ynTwDwFD4PAXgC8BS+uwk7QQELBQA8WYiCLuUVkC+vATwp70ocwKAAgCeDGKi6QgEAT
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MQQQnj6Fcvb5F8lLS175tOBfZ8cfe5R848D9I+UUuEvAPLxGeHI3R0TWPAGEp+aZ0cJtAghPbueH6FojgPDUGjdauU0A4cnt/BBdawTMPSPCU2v8aOUmAYQnN/NCVMkIIDwl40dr9wggPLmXEyJKTgDhKTlDenCPAMKTvZwgPCE82VtN9ASBGAIIT59CWfj8Yrlg0vRPC/511n3dbjJ/zjT5TNeukWsUuEnAPLxGeHIzP0TVGgGEp9a40cpdAghP7uaGyFongPDUOjtauksA4cnd3BBZ6wTMPSPCU+sMaekeAYQn93JCRMkJIDwlZ0gPbhFAeHIrH0RjhwDCkx2O9OIWAYQne/lAeEJ4srea6AkCMQQQntpCuXDqTPnTswvbFq79dNi3viHf/c6gS
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Middle-aged English professors in the 1990s were in their teens and twenties during the Vietnam War \u2014 a formative experience that probably contributed to _The Things They Carried_\u2018s classroom success. Can we test that with OS data? I think so. By the 2010s, the same faculty were retiring and, to an extent, carrying _The Things They Carried_ with them \u2014 out of the classroom, slowly, partially, over the decade. Canonization is durable but not permanent, and the semester calendar forces zero-sum choices in the selection of texts.\\r\\n\\r\\nHarry Potter is a different story \u2014 and not a shocker if one looks at the contexts in which it is assigned. The Harry Potter series is assigned primarily in [children\u2019s literature classes](https://galaxy.opensyllabus.org/#!search/works/harry+potter) \u2014 not the American literature classes where you can find O\u2019Brien and Cisneros, or the world and post-colonial literature classes that assign Roy. Children\u2019s literature has a comparatively large footprint in OS data because it is a subfield of both English and Education. It also shows up in reading and literacy classes \u2014 again, both in the English and Education fields. _The Sorcerer\u2019s Stone_ is the representative text in these contexts. There\u2019s not much classroom digging into the later books, though _Prisoner of Azkaban_ is the distant #2."},{"id":"open-syllabus-announces-new-support-from-the-arcadia-fund","metadata":{"permalink":"/open-syllabus-announces-new-support-from-the-arcadia-fund","source":"@site/blog/2022-09-30-open-syllabus-announces-new-support-from-the-arcadia-fund.md","title":"Open Syllabus announces new support from the Arcadia Fund","description":"Open Syllabus is pleased to announce the receipt of a $1,750,000, 2-year grant from the Arcadia Fund.","date":"2022-09-30T00:00:00.000Z","formattedDate":"September 30, 2022","tags":[{"label":"opensyllabus","permalink":"/tags/opensyllabus"}],"readingTime":0.605,"hasTruncateMarker":false,"authors":[{"name":"Joe Karaganis","url":"https://github.com/karaganis","imageURL":"https://avatars.githubusercontent.com/u/5721537?v=4","key":"joe"}],"frontMatter":{"slug":"open-syllabus-announces-new-support-from-the-arcadia-fund","title":"Open Syllabus announces new support from the Arcadia Fund","authors":["joe"],"tags":["opensyllabus"]},"prevItem":{"title":"Hits of the 1990s","permalink":"/hits-of-the-1990s"},"nextItem":{"title":"So You Want to Write/Tweet Something About the The Communist Manifesto\u2019s High Rank in the OS Dataset","permalink":"/so-you-want-to-write-tweet-something-about-the-the-communist-manifestos-high-rank-in-the-os-dataset"}},"content":"Open Syllabus is pleased to announce the receipt of a $1,750,000, 2-year grant from the Arcadia Fund.\\r\\n\\r\\nThe Arcadia grant will enable Open Syllabus to continue to expand the boundaries of open education by developing new ways to understand and navigate the curriculum in higher education.\xa0 The grant will support work on course transfer and learning outcomes, as well as a range of new tools designed to make OS data a more powerful resource for students, faculty, and lifelong learners.\\r\\n\\r\\nOpe
1n Syllabus is a 501(c)3, US-based, non-profit organization that maps the curriculum in higher education and builds tools to support open education.\\r\\n\\r\\nThe [Arcadia Fund](https://www.arcadiafund.org.uk/) is\xa0a UK charitable organization that supports work on endangered cultural heritage, endangered ecosystems, and open access."},{"id":"so-you-want-to-write-tweet-something-about-the-the-communist-manifestos-high-rank-in-the-os-dataset","metadata":{"permalink":"/so-you-want-to-write-tweet-something-about-the-the-communist-manifestos-high-rank-in-the-os-dataset","source":"@site/blog/2022-05-23-so-you-want-to-write-tweet-something-about-the-the-communist-manifestos-high-rank-in-the-os-dataset.md","title":"So You Want to Write/Tweet Something About the The Communist Manifesto\u2019s High Rank in the OS Dataset","description":"The 4th place rank of The Communist Manifesto among all assigned titles in the OS collection (in the 2.5 dataset) has been written about and referenced frequently. Is this high ranking weird? Suspicious? What\u2019s the story here?","date":"2022-05-23T00:00:00.000Z","formattedDate":"May 23, 2022","tags":[{"label":"opensyllabus","permalink":"/tags/opensyllabus"}],"readingTime":3.19,"hasTruncateMarker":false,"authors":[{"name":"Joe Karaganis","url":"https://github.com/karaganis","imageURL":"https://avatars.githubusercontent.com/u/5721537?v=4","key":"joe"}],"frontMatter":{"slug":"so-you-want-to-write-tweet-something-about-the-the-communist-manifestos-high-rank-in-the-os-dataset","title":"So You Want to Write/Tweet Something About the The Communist Manifesto\u2019s High Rank in the OS Dataset","authors":["joe"],"tags":["opensyllabus"]},"prevItem":{"title":"Open Syllabus announces new support from the Arcadia Fund","permalink":"/open-syllabus-announces-new-support-from-the-arcadia-fund"},"nextItem":{"title":"A National Learning Outcome Map","permalink":"/a-national-learning-outcome-map"}},"content":"The 4th place rank of _The Communist Manifesto_ among all assigned titles in the OS collection (in the 2.5 dataset) has been written about and referenced frequently. Is this high ranking weird? Suspicious? What\u2019s the story here?\\r\\n\\r\\nTo save ourselves some time responding on Twitter, we\u2019ve decided to create a resource for authors and tweeters interested in engaging with this datapoint. The list of tips below doesn\u2019t exhaust the topic of Marx\u2019s role in the curriculum, but hopefully it will help you avoid some common misinterpretations of OS data related to Marx and the Manifesto. Let\u2019s go!\\r\\n\\r\\n*   Why does _The Communist Manifesto_ rank 4th overall? Because it is frequently assigned in multiple fields. It\u2019s 5th in History, 5th in Political Science, 5th in Sociology, 49th in Philosophy, and 153rd in English, 200th in Anthropology. Many titles achieve high rank in one or sometimes two fields. Very few have relevance in three or more. If you study 19th and 20th century history, politics, or social thought, you will at some point be assigned _The Communist Manifesto_\u2013maybe more than once.\\r\\n\\r\\n*   Let\u2019s consider [this talking point](https://www.marketwatch.com/story/communist-manifesto-among-top-three-books-assigned-in-college-2016-01-27): \u201cMarx is the most assigned economist in U.S. college classes.\u201d Be careful with this one. Whether Marx is properly classified as an economist or not, he is not widely assigned in Economics, where the _Manifesto_ ranks 192nd. Nor is his major work of economics, _Capital_, a top title. It ranks 378th overall and 182nd in Economics. For those who worry that Marx has displaced Adam Smith, he hasn\u2019t. _The Wealth of Nations_ is the top-ranked non-textbook title in Economics at #13\\\\. It also does well in History and Political Science.\\r\\n\\r\\n*   In teaching, assigning something is not the same as endorsing it. This should go without saying, but the assumption that _The Communist Manifesto_ is assigned primarily for the purposes of indoctrinating students is a frequent theme in these pieces. We don\u2019t know all the contexts in which the Manifesto is assigned, but a look at [the most common \u2018co-assignments](https://
1opensyllabus.org/result/title?id=8306467210251)\u2018\u2013i.e. the titles it is most frequently assigned with\u2013strongly suggests that its primary teaching role is in classes that explore the canonical texts of Western political thought. What are the top co-assigned titles? Locke\u2019s _Second Treatise on Government_; Hobbes\u2019 _Leviathan_; Smith\u2019s _Wealth of Nations_; Mill\u2019s _On Liberty_; Weber\u2019s _Protestant Ethic and the Spirit of Capitalism_, Machiavelli\u2019s _The Prince_; Plato\u2019s _Republic_. And so on.\\r\\n\\r\\n*   The Manifesto has two other features that don\u2019t come up much in these pieces but which probably significantly affect its rank. First, it\u2019s a famous exercise in rhetoric rather than a treatise. Spend five minutes with it in comparison to any of the titles above and it\u2019s clear why, even in translation, it has a role in literature and writing classes. Second\u2013and probably more important\u2013it\u2019s short. At around 30 pages, it\u2019s very easy to assign in a single class. Compare to the work of excerpting from the 1200 pages of _Wealth of Nations_, or the 650 pages of _Leviathan_, or the 450 of _The Protestant Ethic_ and the classroom advantages are clear.\\r\\n\\r\\n*   Comparative arguments: We urge caution. For those of you who, like the Marketwatch author linked above, want to compare the ubiquity of the Manifesto to the relative invisibility of the Bible in OS data, be aware that we identify works based on title/author combinations, and so struggle with titles that lack attributable authors. All the major religious texts and most of the foundational political documents are in the same boat. For those of you who want to argue that Author X (say, Friedrich Hayek) is a superior thinker to Marx who should be more widely assigned, be aware of the risks of simply reversing the indoctrination argument. The teaching data surrounding _The Communist Manifesto_ pretty clearly describes an effort to understand its historical influence, not increase it."},{"id":"a-national-learning-outcome-map","metadata":{"permalink":"/a-national-learning-outcome-map","source":"@site/blog/2022-02-10-a-national-learning-outcome-map.md","title":"A National Learning Outcome Map","description":"Most of Open Syllabus\u2019s work is built on citation analysis \u2014 on the ability to determine what\u2019s taught and what\u2019s taught together.","date":"2022-02-10T00:00:00.000Z","formattedDate":"February 10, 2022","tags":[{"label":"opensyllabus","permalink":"/tags/opensyllabus"}],"readingTime":2.475,"hasTruncateMarker":false,"authors":[{"name":"Joe Karaganis","url":"https://github.com/karaganis","imageURL":"https://avatars.githubusercontent.com/u/5721537?v=4","key":"joe"}],"frontMatter":{"slug":"a-national-learning-outcome-map","title":"A National Learning Outcome Map","authors":["joe"],"tags":["opensyllabus"]},"prevItem":{"title":"So You Want to Write/Tweet Something About the The Communist Manifesto\u2019s High Rank in the OS Dataset","permalink":"/so-you-want-to-write-tweet-something-about-the-the-communist-manifestos-high-rank-in-the-os-dataset"},"nextItem":{"title":"What is a Field?","permalink":"/what-is-a-field"}},"content":"Most of Open Syllabus\u2019s work is built on citation analysis \u2014 on the ability to determine what\u2019s taught and what\u2019s taught together.\\r\\n\\r\\nBut syllabi contain a lot of other information about teaching and learning. For the past two decades, the coin of the realm in many areas of higher ed has been the \u2018learning outcome,\u2019 conceived as a way to abstract from course contents to an enumerated set of learning goals. Sometimes these are very concrete and specific to a topic:\\r\\n\\r\\n![](./img/students-able-to.webp)\\r\\n\\r\\nSometimes they describe very broad sets of competencies:\\r\\n\\r\\n![](./img/competencies-list.webp)\\r\\n\\r\\nOften, individual learning outcomes are parts of larger frameworks that cover the range of required knowledge for a program or degree. In other cases, faculty rely on learning outcome guidance (such as [Bloom\u2019s taxonomy](https://en.wikipedia.org/wiki/Bloom%27s_taxonomy)) to develop sui generis outcomes for their classes. With a few exceptions, learning outcome frameworks in the US and Canada are defined locally, at the individual program level. This has the advantage of keeping faculty engaged with the frameworks and of keeping outcomes closely tied to student needs. It has the disadvantage, however, of making them useless for comparative or system-level understanding of the curriculum, and for solving problems that require such perspectives. One example of the latter that we\u2019ve been working on is course transfer, in which the lack of common frameworks for understanding class outcomes makes it difficult to establish equivalence between classes.\\r\\n\\r\\nThere are, according to the Lumina Foundation, over 3000 learning outcome frameworks in use in the US. Despite some notable efforts to organize this vast array of material, incentives for programs and schools to pool or coordinate learning outcome development remain weak. Because learning outcomes are generally present in syllabi, however, we may be able to re
1construct the learning outcome landscape from the bottom up. A national map of learning outcomes \u2014 in fact, an international map that includes frameworks from Canada, the UK, and the EU \u2014 may be possible.\\r\\n\\r\\nWe can now pretty reliably extract the individual learning outcomes in syllabi. Our full dataset (currently 10.5 million syllabi) contains around 20 million unique learning outcomes. These can be compared semantically to generate a galaxy-style \u2018map\u2019 of the learning outcome landscape \u2014 here based on a sample of 3 million learning outcomes.\\r\\n\\r\\n![](./img/semantic-embedding-learning-outcomes.webp)\\r\\n\\r\\nThis is preliminary work that builds on relatively simple methods. But it has some interesting features. That wide grey band that runs through the humanities and social sciences suggests a lot of commonality to the learning outcomes in those fields, whereas the sharper boundaries in most STEM and practitioner fields imply more learning outcomes built around field-specific knowledge.\\r\\n\\r\\nOverall, the graph shows enough definition to make us think that we can reverse-engineer a lot of the structure of the underlying frameworks. It won\u2019t be a perfect map, but even a decent one would be leagues better than anything currently available and potentially make possible some of the system-level research and applications that were part of the original promise of learning outcomes."},{"id":"what-is-a-field","metadata":{"permalink":"/what-is-a-field","source":"@site/blog/2022-01-12-what-is-a-field.md","title":"What is a Field?","description":"We take a shot at that question over at Scatterplot.","date":"2022-01-12T00:00:00.000Z","formattedDate":"January 12, 2022","tags":[{"label":"opensyllabus","permalink":"/tags/opensyllabus"}],"readingTime":0.935,"hasTruncateMarker":false,"authors":[{"name":"Joe Karaganis","url":"https://github.com/karaganis","imageURL":"https://avatars.githubusercontent.com/u/5721537?v=4","key":"joe"}],"frontMatter":{"slug":"what-is-a-field","title":"What is a Field?","authors":["joe"],"tags":["opensyllabus"]},"prevItem":{"title":"A National Learning Outcome Map","permalink":"/a-national-learning-outcome-map"},"nextItem":{"title":"David McClure on the Co-Assignment Galaxy","permalink":"/david-mcclure-on-the-co-assignment-galaxy"}},"content":"We take a shot at that question [over at Scatterplot](https://scatter.wordpress.com/2022/01/12/what-is-a-field/).\\r\\n\\r\\n> The Co-Assignment Galaxy represents titles based on the extent to which they appear together on syllabi.\xa0 Each title is a dot whose size is determined by the title\u2019s total assignment count in the collection.\xa0 This simple principle structures a very detailed map of fields, subfields, and their boundaries.\xa0 It also combines what we could call content-based and institutional ways of thinking about fields.\xa0 By content based, I mean that the layout is derived solely from similarities in the assigned contents of millions of classes, with no _a priori_ knowledge about how those classes divide into sociology or history or physics (we added the labels later).\xa0 At the same time, we developed tools that do sort syllabi into the classificatory schemas used by universities, which reflect a more administrative and institutional view of fields. This institutional account shows up in the graph through the use of color.\xa0 A title receives a color based on its predominant field of assignment. Field boundaries and border zones are represented in the Galaxy by this interaction between spatial layout and color."},{"id":"david-mcclure-on-the-co-assignment-galaxy","metadata":{"permalink":"/david-mcclure-on-the-co-assignment-galaxy","source":"@site/blog/2022-01-06-david-mcclure-on-the-co-assignment-galaxy.md","title":"David McClure on the Co-Assignment Galaxy","description":"If you\u2019d like a detailed technical dive into the OS Co-Assignment Galaxy, David McClure has you covered.","date":"2022-01-06T00:00:00.000Z","formattedDate":"January 6, 2022","tags":[{"label":"opensyllabus","permalink":"/tags/opensyllabus"}],"readingTime":0.215,"hasTruncateMarker":false,"authors":[{"name":"Joe Karaganis","url":"https://github.com/karaganis","imageURL":"https://avatars.githubusercontent.com/u/5721537?v=4","key":"joe"}],"frontMatter":{"slug":"david-mcclure-on-the-co-assignment-galaxy","title":"David McClure on the Co-Assignment Galaxy","authors":["joe"],"tags":["opensyllabus"]},"prevItem":{"title":"What is a Field?","permalink":"/what-is-a-field"},"nextItem":{"title":"Architecture","permalink":"/architecture"}},"content":"If you\u2019d like a detailed technical dive into the OS Co-Assignment Galaxy, David McClure has y
1ou covered.\\r\\n<iframe \\r\\n    className=\\"youtube-player\\" \\r\\n    type=\\"text/html\\" \\r\\n    width=\\"640\\" \\r\\n    height=\\"360\\" \\r\\n    style={{ \\r\\n                        display: \'block\',  \\r\\n                        maxWidth: \'1260px\', \\r\\n                        width: \'100%\', \\r\\n                        minHeight: \'587px\', \\r\\n                        maxHeight: \'887px\', \\r\\n                        margin: \'0px\', \\r\\n                        padding: \'0px\', \\r\\n                        border: \'none\'\\r\\n                    }} \\r\\n    src=\\"https://www.youtube.com/embed/avgGz5cYrRA?version=3&amp;rel=1&amp;fs=1&amp;autohide=2&amp;showsearch=0&amp;showinfo=1&amp;iv_load_policy=1&amp;wmode=transparent\\" \\r\\n    allowFullScreen={true}\\r\\n></iframe>"},{"id":"architecture","metadata":{"permalink":"/architecture","source":"@site/blog/2021-12-10-architecture.md","title":"Architecture","description":"Another hi-res, giant field poster you can buy at the OS Print Store. Bring joy to the actual, aspiring, or just nostalgic architect/urbanist in your life for the holidays. Click to explore.","date":"2021-12-10T00:00:00.000Z","formattedDate":"December 10, 2021","tags":[{"label":"opensyllabus","permalink":"/tags/opensyllabus"}],"readingTime":0.165,"hasTruncateMarker":false,"authors":[{"name":"Joe Karaganis","url":"https://github.com/karaganis","imageURL":"https://avatars.githubusercontent.com/u/5721537?v=4","key":"joe"}],"frontMatter":{"slug":"architecture","title":"Architecture","authors":["joe"],"tags":["opensyllabus"]},"prevItem":{"title":"David McClure on the Co-Assignment Galaxy","permalink":"/david-mcclure-on-the-co-assignment-galaxy"},"nextItem":{"title":"The OS Movie Lab","permalink":"/the-os-movie-lab"}},"content":"Another hi-res, giant field poster you can buy at [the OS Print Store](https://open-syllabus.myshopify.com/). Bring joy to the actual, aspiring, or just nostalgic architect/urbanist in your life for the holidays. Click to explore.\\r\\n\\r\\n[![](./img/architecture-blogv.webp)](https://lh3.googleusercontent.com/0mfB29Zkz-KV3yGBRU1SOEk3MH8Q6ADzgXJnTP6Y9IgImiJ1jLfu5j5qRqk61Oew-5dm6o30yyuFeZdUJi8VtVjml7TPMnVGN3nOdn8V3JKravmwfcirQ6AnT0dgk21aCJRX9PnSXA=w2400?source=screenshot.guru)"},{"id":"the-os-movie-lab","metadata":{"permalink":"/the-os-movie-lab","source":"@site/blog/2021-11-22-the-os-movie-lab.md","title":"The OS Movie Lab","description":"We\u2019ve wanted to tease out movie rankings for a while. Movies are maybe the most passionately invested category of Open Syllabus citation data, surrounded by scholarly and popular debates and a teaching field that cuts across many fields. That makes them a rich target for a Lab.","date":"2021-11-22T00:00:00.000Z","formattedDate":"November 22, 2021","tags":[{"label":"opensyllabus","permalink":"/tags/opensyllabus"}],"readingTime":2.845,"hasTruncateMarker":false,"authors":[{"name":"Joe Karaganis","url":"https://github.com/karaganis","imageURL":"https://avatars.githubusercontent.com/u/5721537?v=4","key":"joe"}],"frontMatter":{"slug":"the-os-movie-lab","title":"The OS Movie Lab","authors":["joe"],"tags":["opensyllabus"]},"prevItem":{"title":"Architecture","permalink":"/architecture"},"nextItem":{"title":"Sociology","permalink":"/sociology"}},"content":"We\u2019ve wanted to tease out movie rankings for a while. Movies are maybe the most passionately invested category of Open Syllabus citation data, surrounded by scholarly and popular debates and a teaching field that cuts across many fields. That makes them a rich target for a Lab.\\r\\n\\r\\n[**GO TO THE MOVIE LAB**](/movie-lab/)\\r\\n\\r\\nThe [OS Movie Lab](/movie-lab) (like the earlier [Link Lab](/link-lab/) for journalism) is a navigable ranking of movies taught in college classes\u2013in this case the 1201 movies assigned at least 20 times in the OS corpus since 2015\\\\. It sits outside the core OS toolset because we don\u2019t have a reliable \u2018Movie ID\u2019 in our citation catalogs that would make them a searchable subcategory. Instead, these rankings are built from a hand-curated list derived from the larger OS dataset.\\r\\n\\r\\nThe rankings provide a snapshot of the thinking of hundreds of thousands of faculty about what movies to teach, drawn from millions of syllabi. Many of these decisions clearly belong to a Film Studies-centered discussion about how to teach the history of cinema. But the data comes from all fields and includes choices that reflect a wide array of teaching rationales.\\r\\n\\r\\n![](./img/directors-filtered.webp)\\r\\n\\r\\nOverall, the rankings present a very classical view of film canons and film studies \u2014 still oriented around the post-war American auteurs and the various European waves. Some of this reflects the national biases of the OS collection: around 55% of syllabi are from the US, 15% from the UK and another 10% split between Canada and Australia. The rankings also demonstrate the slowly-changing gender politics of the canon \u2014 and, at a step removed, of access to directorial roles. Women directed 3.5% of the assigned films released prior to 1990\\\\. For films released since 1990, that number is 13%.\\r\\n\\r\\n![](./img/director-gender-1990-graph.webp)\\r\\n\\r\\nThe variety of teaching rationales visible in the rankings is also worth highlighting. Leni Riefenstahl is the highest-ranking woman director at #24, but is most frequently assigned in history classes, not film. Documentaries and historical dramas play roles in to
1pical classes, and literary adaptations do well in literary studies. You can explore the teaching roles of these titles more fully in the [Syllabus Explorer](https://opensyllabus.org/result/title?id=62156766711093). The [Co-Assignment Galaxy](https://galaxy.opensyllabus.org/#!viewport/-4.9379/-0.2278/-12.4042/-9.5411) meanwhile, provides a very different perspective on the organization of film studies and film canons. Lastly, if you\u2019d like to own a giant, beautiful poster of some of this data, [we\u2019ve got you covered](https://open-syllabus.myshopify.com/products/film-and-photography).\\r\\n\\r\\nTime for caveats about the data. OS\u2019s collection is uneven across schools and over-represents Texas within the US; the 2.6 dataset (on which these rankings are built) is bigger and better than the 2.5 dataset but smaller and worse than the upcoming 2.7 version; the underlying citation catalogs (such as the Library of Congress catalog) are messy and often inconsistent; faculty citation practices for film in syllabi are poor; translated titles and director names often have variations; human labeling is error prone; machine learning is imprecise; and on and on. We spend a lot of time on these issues and have gotten better at handling many of them. But they still shape results in a variety of known and unknown ways. Lastly, determining a film\u2019s country of origin becomes complex in the 1990s with the rise of international co-productions\u2013especially in Europe. We have used the first listed country on IMDB in such cases.\\r\\n\\r\\nThe map, in other words, is not the territory. But it\u2019s hopefully a useful guide to it.\\r\\n\\r\\n[**GO TO THE MOVIE LAB**](/movie-lab/)"},{"id":"sociology","metadata":{"permalink":"/sociology","source":"@site/blog/2021-10-31-sociology.md","title":"Sociology","description":"We\u2019ve been featuring the new Open Syllabus posters, which are available at the OS Print Store. Mapping the top 600 or so titles in sociology produced an interesting spatialization of subfields: a tangle of theoretical perspectives in the north, shading into French theory in the east; sexuality in yellow in the southeast; criminology and deviance in red; and a number of race and class-focused subject areas to the west. You can zoom in and explore yourself in this semi-hi-res version. It\u2019s also interesting to compare against the much larger Co-Assignment Galaxy, which maps all fields together.","date":"2021-10-31T00:00:00.000Z","formattedDate":"October 31, 2021","tags":[{"label":"opensyllabus","permalink":"/tags/opensyllabus"}],"readingTime":0.53,"hasTruncateMarker":false,"authors":[{"name":"Joe Karaganis","url":"https://github.com/karaganis","imageURL":"https://avatars.githubusercontent.com/u/5721537?v=4","key":"joe"}],"frontMatter":{"slug":"sociology","title":"Sociology","authors":["joe"],"tags":["opensyllabus"]},"prevItem":{"title":"The OS Movie Lab","permalink":"/the-os-movie-lab"},"nextItem":{"title":"Classics","permalink":"/classics"}},"content":"We\u2019ve been featuring the new Open Syllabus posters, which are available at the [OS Print Store](http://open-syllabus.myshopify.com/). Mapping the top 600 or so titles in sociology produced an interesting spatialization of subfields: a tangle of theoretical perspectives in the north, shading into French theory in the east; sexuality in yellow in the southeast; criminology and deviance in red; and a number of race and class-focused subject areas to the west. You can zoom in and explore yourself in this semi-hi-res version. It\u2019s also interesting to compare against the much larger [Co-Assignment Galaxy](https://galaxy.opensyllabus.org/#!viewport/-5.1729/0.5573/0.7817/4.2649), which maps all fields together.\\r\\n\\r\\n[![](./img/sociology-blogv.webp)](https://lh3.googleusercontent.com/RiJk1liWPYNyL--zzVUr_dbl8p1F2qDCLjrSESZ8mM2_QJEJr6JpIVd9Jqnd_OfRcAJPL3bMJ3BHj6jo7rrZWGaKoDuRvPf1q0JqA00YocnOSfxQ5bU_raTJDMgpCn_6DIWvN7vKsg=w2400?source=screenshot.guru) \\r\\n\\r\\nThese are very large (36\u2033x48\u2033) posters available for $54.99"},{"id":"classics","metadata":{"permalink":"/classics","source":"@site/blog/2021-10-27-classics.md","title":"Classics","description":"Following up with another highish-res poster image, here\u2019s Classics (available here as a very large 36\u2033x48\u2033 format print)","date":"2021-10-27T00:00:00.000Z","formattedDate":"October 27, 2021","tags":[{"label":"opensyllabus","permalink":"/tags/opensyllabus"}],"readingTime":0.305,"hasTruncateMarker":false,"authors":[{"name":"Joe Karaganis","url":"https://github.com/karaganis","imageURL":"https://avatars.githubusercontent.com/u/5721537?v=4","key":"joe"}],"frontMatter":{"slug":"classics","title":"Classics","authors":["joe"],"tags":["opensyllabus"]},"prevItem":{"title":"Sociology","permalink":"/sociology"},"nextItem":{"title":"The Galaxy of Philosophy","permalink":"/the-galaxy-of-philosophy"}},"content":"Following up with another highish-res poster image, here\u2019s Classics ([available here](http://open-syllabus.myshopify.com/) as a very large 36\u2033x48\u2033 format print)\\r\\n\\r\\n[![](./img/classics-blogv.webp)](https://lh3.googleusercontent.com/ylwE1QMBBt5x1VP2yx2w43yetkQvTy7cGNnCY28a8ma9Z40UDoyo1ccUxuO9iyBMPx2jBVxaC--Q2X1Pb_Vl-1dF0dJl7RZTQDmrd0tCcCWvl-fpYfN3CDZr0ERi1n0QaHJRU1iCDg=w2400?source=screenshot.guru) \\r\\n\\r\\nVery roughly, it divides into Greek literature in the south (in green) and art and religion in the north (in blue). Rome is in red and purple, with art and architecture on the right and family and gender relations on the left."},{"id":"the-galaxy-of-philosophy","metadata":{"permalink":"/the-galaxy-of-philosophy","source":"@site/blog/2021-10-26-the-galaxy-of-philosophy.md","title":"The Galaxy of Philosophy","description":"Last month we launched a print store with 11 \u2018field posters\u2019 designed by the great Nadieh Bremer. The
1se are very large 36\u2033x48\u2033 high-res posters that map the top 600 or so titles in different fields. The size of a dot indicates how often a title is assigned. Titles cluster and are colored based on how often they are assigned together.","date":"2021-10-26T00:00:00.000Z","formattedDate":"October 26, 2021","tags":[{"label":"opensyllabus","permalink":"/tags/opensyllabus"}],"readingTime":1.055,"hasTruncateMarker":false,"authors":[{"name":"Joe Karaganis","url":"https://github.com/karaganis","imageURL":"https://avatars.githubusercontent.com/u/5721537?v=4","key":"joe"}],"frontMatter":{"slug":"the-galaxy-of-philosophy","title":"The Galaxy of Philosophy","authors":["joe"],"tags":["opensyllabus"]},"prevItem":{"title":"Classics","permalink":"/classics"},"nextItem":{"title":"Business School \u2018Teaching Power\u2019","permalink":"/business-school-teaching-power"}},"content":"Last month we launched [a print store](https://open-syllabus.myshopify.com/) with 11 \u2018field posters\u2019 designed by the great [Nadieh Bremer](https://www.visualcinnamon.com/). These are very large 36\u2033x48\u2033 high-res posters that map the top 600 or so titles in different fields. The size of a dot indicates how often a title is assigned. Titles cluster and are colored based on how often they are assigned together.\\r\\n\\r\\nIn addition to being gorgeous, our bet is that they are also instructive for students looking to develop an overall grasp of complex fields. Posters cost [$54.99](https://open-syllabus.myshopify.com/products/philosophy) and sales support the work of Open Syllabus.\\r\\n\\r\\nWe\u2019ll feature these over the next month or two. Here\u2019s Philosophy. You can click and zoom \u2014 though the resolution is a bit low.\\r\\n\\r\\n[![](./img/philosophy-blogv.webp)](https://lh3.googleusercontent.com/F3fMSMY5NU7d28QmaIIGYU8z9hdYDUTZjIcjoyEXblht9XTfibURg7ZeGaXl_pmM3tkGeOppMXEHChvGSk0JWgOWWMq5Rcx1wAMiAlYB58cBT2aMdummhZefchabOgkh1SMnFc8I-Q=w2400?source=screenshot.guru) \\r\\n\\r\\nOne interesting thing about this layout, from my perspective, is that it doesn\u2019t strongly reproduce my mental map of the field \u2014 which was formed through a political theory education that privileged a division between anglo/analytic and continental traditions. You can find those divisions, but the field overall includes a lot of \u2018cross canonical\u2019 works that are taught across multiple themes and traditions. It\u2019s also interesting to compare to [the place of philosophy in the larger \u2018co-assignment galaxy,\u2019](https://galaxy.opensyllabus.org/#!viewport/-4.5578/1.1724/3.5850/7.0373) which maps a much wider array of titles across syllabi from all fields."},{"id":"business-school-teaching-power","metadata":{"permalink":"/business-school-teaching-power","source":"@site/blog/2021-05-31-business-school-teaching-power-check-full-chart.md","title":"Business School \u2018Teaching Power\u2019","description":"Each year, the Financial Times ranks international MBA programs. This year the top 10 are Harvard, Stanford, INSEAD (France/Singapore), U Penn \u2013 Wharton, London Business School, CEIBS (China), U Chicago \u2013 Booth, MIT \u2013 Sloan, Columbia, IESE (Spain), Yale, and Northwestern \u2013 Kellogg.","date":"2021-05-31T00:00:00.000Z","formattedDate":"May 31, 2021","tags":[{"label":"opensyllabus","permalink":"/tags/opensyllabus"}],"readingTime":4.4,"hasTruncateMarker":true,"authors":[{"name":"Joe Karaganis","url":"https://github.com/karaganis","imageURL":"https://avatars.githubusercontent.com/u/5721537?v=4","key":"joe"}],"frontMatter":{"slug":"business-school-teaching-power","title":"Business School \u2018Teaching Power\u2019","authors":["joe"],"tags":["opensyllabus"]},"prevItem":{"title":"The Galaxy of Philosophy","permalink":"/the-galaxy-of-philosophy"},"nextItem":{"title":"Galaxy v2","permalink":"/galaxy-v2"}},"content":"Each year, the Financial Times ranks international MBA programs. This year [the top 10](https://www.ft.com/content/eb15f219-aac6-42ca-b9c0-f5b803350903) are Harvard, Stanford, INSEAD (France/Singapore), U Penn \u2013 Wharton, London Business Sc
1hool, CEIBS (China), U Chicago \u2013 Booth, MIT \u2013 Sloan, Columbia, IESE (Spain), Yale, and Northwestern \u2013 Kellogg.\\r\\n\\r\\nSchool ranking methods are often controversial: schools, educational goals, and concepts of quality vary considerably and the data and inputs into the models are often subjective. ([Here\u2019s Malcolm Gladwell](https://www.newyorker.com/magazine/2011/02/14/the-order-of-things) on an older version of the _US News & World Report_ ranking methodology). The FT mitigates these problems by focusing on a set of more narrowly comparable institutions (top tier MBA programs), on relatively unambiguous inputs (it heavily weights student outcomes measured in salary and program value in cost), and by limiting scoring to a top 10 (instead of elevating the minor differences that separate schools in the long tail). FT includes a \u201cresearch rank\u201d derived from the number of articles that faculty published in top journals, but salary factors outweigh research factors by 4-1\\\\. And it lightly weights a host of other variables \u2014 gender ratios, international diversity, faculty PhDs, and so on \u2014 that contribute to the quality of a business education.\\r\\n\\r\\n\x3c!--truncate--\x3e\\r\\n\\r\\n[In collaboration with the FT](https://www.ft.com/content/beb77be1-f735-45e9-82cb-ec834eb39565), we\u2019ve been exploring whether Open Syllabus data can capture a different dimension of school quality: the degree to which a business school\u2019s faculty influence the teaching of business in general, throughout the field. Our method is simple: we took the 500 most frequently assigned texts from the 595,000 business and econ classes we\u2019ve collected since 2015, counted the number of times each text is assigned, awarded an equal portion of those assignments to each author (when \u2014 as is often the case \u2014 there is more than one), linked each author back to a primary home institution, and counted up the totals assignments attributable to the business schools among them. Here\u2019s what that ranking looks like \u2014 with the contributions of the individual authors visible.\\r\\n\\r\\nimport BusinessSchoolTeaching from \'@site/src/components/tableau-business-school-teaching\';\\r\\n\\r\\n<BusinessSchoolTeaching></BusinessSchoolTeaching>\\r\\n\\r\\n([full-size chart](https://public.tableau.com/profile/joe.karaganis2705#!/vizhome/BusinessSchoolTeaching/Sheet1?publish=yes))\\r\\n\\r\\nThis is a model that heavily rewards textbook authors and solo business gurus. With the exception of Harvard, which has a number of successful examples of both, textbook authorship is paramount in the rankings. There is comparatively little research and only a handful of articles represented in the top assigned titles in the business curriculum.\\r\\n\\r\\nThe results clearly overlap the FT rankings but also diverge in interesting ways that are suggestive of the complex role that teaching \u2014 and writing for teaching \u2014 plays in academic reputation. Broadly, we view both as chronically undervalued, and think this undervaluation is reinforced by the the scarcity of ways to measure significant contributions (notably compared to citation metrics for research). At the same time, textbook authorship plays a role in the self-branding activities of prominent faculty, and so in academic mobility and school reputation. Our hunch is that our data reveals a a previously invisible middle ground of schools with fewer \u2018stars\u2019 but more contributors to textbook authorship. But we will defer to people with more knowledge of business schools to interpret the results.\\r\\n\\r\\nWe also spent some time labeling the gender of assigned authors. Here the results are an unambiguous indictment of the field. Among all assignments by business school faculty, just under 10% are authored by women.\\r\\n\\r\\n![](https://blog.opensyllabus.org/wp-content/uploads/2021/05/Screen-Shot-2021-05-24-at-10.20.16-AM.png)\\r\\n\\r\\nWe\u2019d make a couple points beyond the obvious. First, for a field oriented toward contemporary practice, the business curriculum is old \u2014 heavily populated by textbooks originally written 20 and 30 years ago and by authors in or past retirement. Second, we don\u2019t have a good handle on how well or poorly other fields do. A similar [analysis by Kristin Thomson](https://medium.com/@kristinthomson/a-brief-
1analysis-of-class-assignments-814c6b3bca99) of sociology titles from several years ago put the ratio at around 80/20 \u2014 in a field with a much more substantial role for historical titles.\\r\\n\\r\\nWithout doubt, our method is imperfect. People move around, making it hard \u2014 sometimes \u2014 to identify a primary affiliation (though late career posts proved surprisingly stable). Textbooks sometimes change co-authors over multiple editions, and the bibliographical records of these changes can be inconsistent. We didn\u2019t count authors with primary affiliations in Econ departments but did credit business school contributions to the Econ curriculum (since business schools teach economics and sometimes employ economists.) We didn\u2019t count the large number of independent authors, who make up a significant portion of assignments.\\r\\n\\r\\nIf you want to see what this would look like with different criteria or more time invested in labeling data, you\u2019re welcome to [roll your own school rankings.](https://docs.google.com/spreadsheets/d/1cj1iwC5yEYObCXBughcfCmBlnPshR9TgkyhWwk5Shtk/edit?usp=sharing) If you\u2019d like to see what schools we draw on in mapping the curriculum, you can explore the different [field>school lists](https://opensyllabus.org/results-list/fields?size=100) here.\\r\\n\\r\\nIf you\u2019d like to see your own school better represented, we encourage you to either [send us your own syllabi](https://blog.opensyllabus.org/share-syllabi/) or [get in touch about getting your institution on board](mailto:[email protected]).\\r\\n\\r\\nps. Alistair Brandon-Jones wrote in to offer a couple corrections, including our use of an older edition of _Operations Management_ in our author attributions. Our methods do a bad job distinguishing editions, but since in this case the editions have somewhat different authors, we\u2019ve shifted to the editions likely to have been most assigned in the period under question \u2014 2015-2021\\\\. In practice, this drops Warwick a few places and elevates Bath."},{"id":"galaxy-v2","metadata":{"permalink":"/galaxy-v2","source":"@site/blog/2021-02-10-galaxy-v2.md","title":"Galaxy v2","description":"Today we\u2019re excited to release a big update to the Galaxy visualization, an interactive UMAP plot of graph embeddings of books and articles assigned in the Open Syllabus corpus! (This is using the new v2.5 release of the underlying dataset, which also comes out today.) The Galaxy is an attempt to give a 10,000-meter view of the \u201cco-assignment\u201d patterns in the OS data \u2013 basically, which books and articles are assigned together in the same courses. By training node embeddings on the citation graph formed from (syllabus, book/article)\xa0edges, we can get really high-quality representations of books and articles that capture the ways in which professional instructors use them in the classroom \u2013 the types of courses they\u2019re assigned in, the other books they\u2019re paired with, etc.","date":"2021-02-10T00:00:00.000Z","formattedDate":"February 10, 2021","tags":[{"label":"opensyllabus","permalink":"/tags/opensyllabus"}],"readingTime":11.285,"hasTruncateMarker":true,"authors":[{"name":"David McClure","url":"https://github.com/davidmcclure","imageURL":"https://avatars.githubusercontent.com/u/814168?v=4","key":"david"}],"frontMatter":{"slug":"galaxy-v2","title":"Galaxy v2","authors":["david"],"tags":["opensyllabus"]},"prevItem":{"title":"Business School \u2018Teaching Power\u2019","permalink":"/business-school-teaching-power"},"nextItem":{"title":"Welcome to Open Syllabus 2.5","permalink":"/welcome-to-open-syllabus-2-5"}},"content":"Today we\u2019re excited to release a big update to the [Galaxy visualization](http://galaxy.opensyllabus.org/), an interactive UMAP plot of graph embeddings of books and articles assigned in the Open Syllabus corpus! (This is using the new v2.5 release of the underlying dataset, which also comes out today.) The Galaxy is an attempt to give a 10,000-meter view of the \u201cco-assignment\u201d patterns in the OS data \u2013 basically, which books and articles are assigned together in the same courses. By training node embeddings on the citation graph formed from `(syllabus, book/article)`\xa0edges, we can get really high-quality representations of books and articles that capture the ways in which professional instructors use them in the classroom \u2013 the types of courses they\u2019re assigned in, the other books they\u2019re paired with, etc.\\r\\n\\r\\nThe new version is a pretty big upgrade from before, both in terms of the size of the slice of the underlying citation graph that we\u2019re operating on, and the capabilities of the front-end plot viewer. The plot now contains the 1,138,841 most frequently-assigned books and articles in the dataset (up from 160k before) and shows 500,000 points on the screen at once (up from 30k before).\\r\\n\\r\\n\x3c!-- truncate --\x3e\\r\\n\\r\\n[http://galaxy.opensyllabus.org/](http://galaxy.opensyllabus.org/)\\r\\n\\r\\n[![](./img/landing-osp.webp)](http://galaxy.opensyllabus.org/)\\r\\n\\r\\nUnder the hood, this is a pretty straightforward transformation of the raw citation graph that comes out of the OS data pipeline. The citation extractor identifies references to books and articles in the syllabus, which can take a few different forms \u2013 lists of required books, week-by-week reading assignments, bibliographies, etc. Eg, from\xa0[\u201cStatistical Learning Theory and Applications\u201d at MIT](https://www.mit.edu/~9.520/fall19/):\\r\\n\\r\\n![](./img/mit-9520.webp)\\r\\n\\r\\nOr,\xa0[\u201cIntroduction to African American Literature\u201d at Stanford](https://english.stanford.edu/sites/english/files/courses/syllabus/143.docx):\\r\\n\\r\\n![](./img/stanford-eng43.webp)\\r\\n\\r\\nOnce we extract these citations and link them to canonical bibliographic records, we get a bipartite graph over syllabi and books/articles (we call them \u201cworks\u201d). Eg, say we have:\\r\\n\\r\\n*   Course 1, which assigns works 1, 2, 3\\r\\n*   Course 2, which assigns works 3, 4\\r\\n*   Course 3, which assigns works 4, 5, 6\\r\\n\\r\\nThe edges contributed by each syllabus look like:\\r\\n\\r\\n![](./img/courses-separate.
1webp)\\r\\n\\r\\nAnd then, wiring everything up into a single graph:\\r\\n\\r\\n![](./img/courses-merged.webp)\\r\\n\\r\\n**node2vec \u2192 UMAP**\\r\\n\\r\\nThe full version of this graph in the v2.5 data quite big \u2013 4,330,717 syllabus nodes, 4,819,773 book/article nodes, and 39,532,201 edges, where each edge represents a single instance of a work getting assigned in a course. For the Galaxy, though, we crop this down quite a bit, in an effort to get a really clean set of inputs \u2013 we remove syllabi have a very large (> 50) or very small (< 4) number of assignments. (Both of which, in different ways, seem to muddy up the structure of the final UMAP layouts \u2013 this is heuristic, though.) After this filtering, we end up with an undirected graph with 3,423,693 nodes (1,142,666 syllabi, 2,281,027 works) and 13,982,096 edges. Then, we fit a node2vec model on this to get node embeddings for the syllabi and works. After trying a couple different options, we ended up using the\xa0[C++ implementation in the original SNAP project](https://github.com/snap-stanford/snap/tree/master/examples/node2vec). On a pretty big EC2 instance (`r5a.16xlarge`, 64 cores), this takes about 3 hours to fit:\\r\\n\\r\\n    node2vec -i:graph.edgelist -o:graph.emb -d:64 -l:40 -q:0.5\\r\\n\\r\\nThis gives us a 64d embedding for each syllabus and work, a distributed representation of the \u201cposition\u201d of each inside the citation graph. From this full 3,423,693 x 64d embedding matrix, we slice out the embeddings for just the work nodes, and, to clean things up a bit more, also drop out works that appear fewer than 3 times in the overall dataset. (So \u2013 we treat the syllabi as a sort of glue that binds together the books and articles for the purpose of the graph embedding; but then pull out just the representations for the books and articles.) This leaves 1,138,841 x 64d embeddings, where each represents one work assigned 3+ times in the filtered set of syllabi.\\r\\n\\r\\nFinally \u2013 we project this down to 2d with UMAP, using the\xa0[GPU-accelerated implementation in RAPIDS](https://docs.rapids.ai/api/cuml/nightly/api.html#umap), which is really fantastic for this kind of dataset \u2013 on a T4 (`g4dn.8xlarge`\xa0EC2 node), the UMAP models fit in the range of ~4-5 minutes on the ~1Mx64 input embedding. This is awesome, and made it possible to experiment with UMAP parameters in a much more wide and systematic way than would have been possible otherwise. (I wish the same were true for the initial node2vec fit\u2026) After running a bunch of parameter sweeps, we settled on:\\r\\n\\r\\nreducer = UMAP(\\r\\n    n_neighbors=100,\\r\\n    n_epochs=1000,\\r\\n    negative_sample_rate=20,\\r\\n)\\r\\n\\r\\n![](./img/umap-raw.webp)\\r\\n\\r\\nWe then do a bit of final preprocessing \u2013 we rotate the layout to an orientation that\u2019s visually pleasing (not ahem, the most scientific step), and assign colors to the points based on the fields that the work is assigned in. For the purpose of the visualization, a work is assigned to a field if >50% of its assignments are in courses from a single field. Otherwise, we color it gray, and label it as \u201cMultiple Fields.\u201d (Which, it turns out, makes it possible to find some really interesting interdisciplinary clusters in the embedding space \u2013 eg, environmental studies, media studies, and a nexus of sociology / philosophy / critical theory.)\\r\\n\\r\\n![](./img/umap-staged.webp)\\r\\n\\r\\n**WebGL scatterplots**\\r\\n\\r\\nOnce the layout is in place \u2013 the process of rendering it interatively in the browser becomes, in effect, a web mapping project, similar in many ways to how you might show literal (geo)spatial data with Leaflet / Mapbox / OpenLayers. We\u2019ve got dataset laid out on a 2d grid, and want to make it possible to\xa0_navigate_\xa0the space \u2013 figure out what\u2019s where, zoom in and out, inspect individual \u201clocations,\u201d grok the overall structure of the space. Except, geographic space is swapped out for the abstract space of the UMAP embedding.\\r\\n\\r\\nThe first step \u2013 like with (literal) map data, the plot is too big to load into the browser in bulk on startup \u2013 the full dataset of 1.1M points is ~80m as gzipped JSON. So, we need some kind of \u201ctiling\u201d strategy, where a smaller slice of the data is loaded initially, and then more detail is filled in on-demand as the user zooms in on particular regions of the plot. We first index all of the points in Elasticsearch using a\xa0[`geo_point`\xa0field](https://www.elastic.co/guide/en/elasticsearch/reference/current/geo-point.html)\xa0to store the coordinates (along with the bibliographic metadata for each work, which is used later for full-text search). Then, to move data into the browser, we split the plot into two parts \u2013 a \u201cforeground\u201d set of 500k points, which is cached as compressed JSON (using brotli, which is a significant boost over gzip) and downloaded into the client as a single payload on 
1startup. Then, to surface the points in the \u201cbackground\u201d set \u2013 when the viewport zooms below a certain threshold, we start to fire off bounding-box queries to Elasticsearch, which returns the first N points from the background set inside of the current viewport. So, if you zoom far enough \u2013 all of the 1.1M points are \u201cdiscoverable\u201d; but we cap the initial data pull at ~20mb.\\r\\n\\r\\n(Which is still bigger than I\u2019d like, really \u2013 I\u2019m interested in playing with ways of \u201cstaging\u201d this in more sophisticated ways, maybe by initially just loading the raw position / size / color attributes that are needed to draw the plot, and then loading the other metadata in some kind of deferred or on-demand way.)\\r\\n\\r\\nOnce points are in the browser, we draw them using as a single\xa0`gl.POINTS`\xa0primitive (via\xa0[regl](https://github.com/regl-project/regl)), which is super fast. Picking is done with the coloring trick \u2013 when a pan or zoom gesture ends and the viewport locks in a new position, a second copy of the current frame is drawn into a framebuffer using a separate fragment shader that colors each point using a unique hex color that corresponds to offset position of the point in the attribute buffers. Then, when the cursor moves, we read off the pixel at the current mouse position from the framebuffer, and map the color back to the offset position, and then back to the point object itself. (This is described in detail\xa0[here](https://threejsfundamentals.org/threejs/lessons/threejs-picking.html), in the context of Three.js.)\\r\\n\\r\\nFor the pan and zoom mechanics, we\u2019re using a technique that I learned from Ben Schmidt \u2013 use\xa0[d3-zoom](https://github.com/d3/d3-zoom)\xa0to listen for the gesture events on the container element, and then pass the\xa0`(x, y, k)`\xa0zoom transform to the vertex shader as a uniform, which can then be used to set\xa0`gl_Position`\xa0and\xa0`gl_PointSize`. (See Ben\u2019s description\xa0[here](https://observablehq.com/@bmschmidt/sharing-a-single-zoom-state-between-svg-canvas-and-webgl).) This works great \u2013 it\u2019s easy to implement and super fast. And, as Ben points out, maybe the biggest advantage is that this makes it really easy to layer regular 2d canvas/svg elements that \u201cmove\u201d with the data on the plot \u2013 just listen for changes to the zoom transform, and then apply it to canavas/svg elements stacked on top of the plot. We\u2019re making use of this pretty heavily here, for most of the \u201cannotations\u201d that get drawn on top of the raw points. Eg, the red highlight points that appear on hover / click, and point labels that are automatically displayed for the N largest visible works \u2013\\r\\n\\r\\n![](./img/hl-points-labels.webp)\\r\\n\\r\\nThe \u201cgeometric\u201d labels that zoom with the data \u2013\\r\\n\\r\\n![](./img/geom-anns.webp)\\r\\n\\r\\nAnd, the \u201cheatmap\u201d overlay that highlights the location of the search results on the plot \u2013\\r\\n\\r\\n![](./img/search-hl.webp)\\r\\n\\r\\nI really like this pattern \u2013 it gives you access to the whole ecosystem of 2d APIs and libraries, which is often a much better fit for this kind of stuff (eg, drawing text in WebGL is a headache). And, it also makes it easy to structure the code in a really decoupled, modular way \u2013 each of these \u201clayers\u201d can be written as an independent plugin, in effect, which just needs to subscribe to a couple of events on the plot.\\r\\n\\r\\n**Topic search**\\r\\n\\r\\nLast but not least \u2013 in addition to the updates to the dataset and plot viewer, we also added one significant new piece of functionality: A new way of searching inside the plot, which is sort of a sandbox for a way of navigating the OS data that we\u2019re interested in exploring more deeply in the future. Before, you could just do a direct metadata search over the title and author strings for the works in the plot. (This is still there as the default search result type, under \u201csearch books.\u201d) This makes it easy to find specific books or authors if you already know what you\u2019re looking for. And, it also works OK for certain kinds of \u201ctopic\u201d-like queries where there are lots of books with titles that literally contain the keyword(s) in question. Eg, for something like \u201clinear algebra\u201d \u2013 there are a bunch of textbooks that have \u201clinear algebra\u201d in the title, so just by searching against the titles in the catalog, you get a good sense of where in the plot to look.\\r\\n\\r\\nBut, for other kinds of queries, this doesn\u2019t work as well. Eg, for something like \u201cscience fiction\u201d \u2013 if you search against the book titles directly, you mostly get anthologies and academic monographs; but very few actual works of science fiction \u2013\\r\\n\\r\\n![](./img/sf-works.webp)\\r\\n\\r\\nWhat you really want, for this kind of query, is to search\xa0_inside of the syllabi themselves_\xa0\u2013 the course descriptions, titles, learning objectives, topic lists, schedules, etc. \u2013 and then build a reading list based on the books and articles assigned in the most relevant courses. To experiment with this, we added the \u201cSearch Syllabi\u201d result type in the left panel that does just this. (Though currently in a somewhat limited way \u2013 we just search against the course titles and description paragraphs, for a subset of the corpus.) Eg, for \u201cscience fiction,\u201d we start to get a much better view of the primary texts in the field \u2013\\r\\n\\r\\n![](./img/sf-courses.webp)\\r\\n\\r\\nThis starts to open the door to a more granular, \u201csliced\u201d view of the OS data that I\u2019ve always wanted. Other examples \u2013\\r\\n\\r\\n*   [`media theory`](https://galaxy.opensyllabus.org/#!search/courses/media+theory)\\r\\n*   [`developmental psychology`](https://galaxy.opensyllabus.org/#!search/courses/developmental+psychology)\\r\\n*   [`black lives matter`](https://galaxy.opensyllabus.org/#!search/courses/black+lives+matter)\\r\\n*   [`climate change`](https://galaxy.opensyllabus.org/#!search/courses/climate+change)\\r\\n*   [`african american literature`](https://galaxy.opensyllabus.org/#!search/courses/afr
1ican+american+literature)\\r\\n*   [`cognitive science`](https://galaxy.opensyllabus.org/#!search/courses/cognitive+science)\\r\\n*   [`russian literature`](https://galaxy.opensyllabus.org/#!search/courses/russian+literature)\\r\\n\\r\\nIt\u2019s also interesting to plug in more generic, open-ended terms, and see what comes out \u2013\\r\\n\\r\\n*   [`theory`](https://galaxy.opensyllabus.org/#!search/courses/theory)\\r\\n*   [`praxis`](https://galaxy.opensyllabus.org/#!search/courses/praxis)\\r\\n*   [`writing`](https://galaxy.opensyllabus.org/#!search/courses/writing)\\r\\n*   [`reading`](https://galaxy.opensyllabus.org/#!search/courses/reading)\\r\\n*   [`past`](https://galaxy.opensyllabus.org/#!search/courses/past)\\r\\n*   [`future`](https://galaxy.opensyllabus.org/#!search/courses/future)\\r\\n\\r\\nAnyway, this is sort of a sandbox version of this functionality to figure out if it\u2019s useful. We\u2019d love feedback about whether this works \u2013 if it\u2019s intuitive, if the results are interesting.\\r\\n\\r\\n**Prior art + inspiration**\\r\\n\\r\\nThere\u2019s been a lot of really cool work in the last couple years at the overlap of UMAP, WebGL-powered scatterplots, and \u2013 more broadly \u2013 large-scale visualizations of latent spaces. In particular, I\u2019ve learned and borrowed a huge amount from \u2013\\r\\n\\r\\n*   Leland McInnes \u2013\xa0[UMAP](https://umap-learn.readthedocs.io/en/latest/), of course!\\r\\n*   Ben Schmidt\u2019s\xa0[visualization of the HathiTrust catalog](http://creatingdata.us/datasets/hathi-features/), and other scatterplot experiments; especially for pointing me towards regl and the d3-zoom pattern described\xa0\u261d\ufe0f.\\r\\n*   Peter Beshai\u2019s\xa0[blog posts about using regl to animate big scatterplots](https://peterbeshai.com/blog/2017-05-26-beautifully-animate-points-with-webgl-and-regl/); especially some of the shader logic for converting from pixel -> NDC coordinates.\\r\\n*   Doug Duhaime\u2019s\xa0[PixPlot](https://dhlab.yale.edu/projects/pixplot/)\xa0and\xa0[UMAP zoo](https://duhaime.s3.amazonaws.com/apps/umap-zoo/index.html); and other conversations about webgl tradecraft.\\r\\n*   Grant Custer\u2019s\xa0[UMAP visualization of MNIST](https://grantcuster.github.io/umap-explorer/), which first got me interested in webgl.\\r\\n*   Emily Reif\u2019s\xa0[dynamically-generated plots of BERT word embeddings](https://pair-code.github.io/interpretability/context-atlas/blogpost/).\\r\\n*   Max Noichl\u2019s beautiful UMAP plots of citation data in\xa0[philosophy](https://homepage.univie.ac.at/maximilian.noichl/posts/structure-of-recent-philosophy-ii/)\xa0and\xa0[economics](https://homepage.univie.ac.at/maximilian.noichl/full/econ/econ2.html).\\r\\n*   Colin Eberhardt\u2019s\xa0[treatment of Ben Schmidt\u2019s HTRC plot](https://blog.scottlogic.com/2020/05/01/rendering-one-million-points-with-d3.html).\\r\\n*   Shan Carter, Zan Armstrong, Ludwig Schubert, Ian Johnson, Chris Olah \u2013\xa0[UMAP plots of \u201cfeature visualizations\u201d extracted from image classifiers](https://distill.pub/2019/activation-atlas/).\\r\\n*   Matt Miller\u2019s\xa0[network visualizations of the NYPL catalog data](https://www.nypl.org/blog/2014/07/31/networked-catalog)\xa0back in 2014, which blew my mind and initially got me interested in these kinds of large-scale knowledge mapping projects.\\r\\n*   And, I\u2019m sure many more things that have come across my Twitter feed, but I\u2019m forgetting now."},{"id":"welcome-to-open-syllabus-2-5","metadata":{"permalink":"/welcome-to-open-syllabus-2-5","source":"@site/blog/2021-02-10-welcome-to-open-syllabus-2-5.md","title":"Welcome to Open Syllabus 2.5","description":"Today we\u2019re releasing a big update to Open Syllabus data and websites. Here\u2019s a rundown:","date":"2021-02-10T00:00:00.000Z","formattedDate":"February 10, 2021","tags":[{"label":"opensyllabus","permalink":"/tags/opensyllabus"}],"readingTime":2.22,"hasTruncateMarker":false,"authors":[{"name":"Joe Karaganis","url":"https://github.com/karaganis","imageURL":"https://avatars.githubusercontent.com/u/5721537?v=4","key":"joe"}],"frontMatter":{"slug":"welcome-to-open-syllabus-2-5","title":"Welcome to Open Syllabus 2.5","authors":["joe"],"tags":["opensyllabus"]},"prevItem":{"title":"Galaxy v2","permalink":"/galaxy-v2"},"nextItem":{"title":"About that Nobel Prize\u2026","permalink":"/about-that-nobel-prize"}}
1,"content":"Today we\u2019re releasing a big update to Open Syllabus data and websites. Here\u2019s a rundown:\\r\\n\\r\\n**[The Co-Assignment Galaxy](https://galaxy.opensyllabus.org)**\\r\\n\\r\\nThe Galaxy has received a massive upgrade in scale and functionality. The previous version mapped 164,000 titles and could display 30,000 at a time. The new version maps 1.1 million titles and can display 500,000 at a time. The resolution of fields and subfields is vastly improved as a result.\\r\\n\\r\\nThe Galaxy also implements a much-requested \u2018search by topic\u2019 function, which searches against the full text of syllabi rather than titles and authors\u2013though you can still do that too. Results are now heat mapped to help users zoom in on areas of interest. David McClure has written up [a detailed technical post](https://blog.opensyllabus.org/?p=1974) on the new Galaxy for those who want a look under the hood.\\r\\n\\r\\n**[OER Metrics](https://oer.opensyllabus.org)**\\r\\n\\r\\nOER Metrics is a new subsite for investigating trends and adoption patterns for openly-licensed books and textbooks (i.e., Open Educational Resources). It provides the first tools for mapping the demand side of the OER ecosystem and\u2013we hope\u2013can help inform adoption decisions by instructors and programs and investment decisions by authors, publishers, and funders.\\r\\n\\r\\n**[Link Lab](/link-lab/)**\\r\\n\\r\\nLink Lab is an exploration of \u2018non-traditional\u2019 teaching materials in the collection identified by URLs in the syllabi. These links are then walked back to their source to collect titles, authors, and other metadata. Link Lab picks up newspaper and magazine stories, videos and documentaries, blog posts, and other materials that are frequently taught but rarely recognized or curated as teaching materials. We are working on integrating URL identification into the main dataset. In the meantime, the preliminary data is presented here as a Lab.\\r\\n\\r\\n**[The 2.5 Dataset](https://docs.opensyllabus.org)**\\r\\n\\r\\nThe Syllabus Explorer, Galaxy, and other services are now using version 2.5 of the OS dataset, which represents a big improvement over the previous 2.0 version. Among the highlights:\\r\\n\\r\\nIt adds 2018 data, bringing the total to 7.2 million syllabi.\\r\\n\\r\\nThis number reflects significant overall growth due to better collecting techniques and also better deduplication techniques. Because the latter outweighed the former, we saw a net gain of 500K syllabi through 2017 (the period of the 2.0 collection).\\r\\n\\r\\nThe 2.5 collection has a much larger reference catalog that enables the identification of many more titles: 4.6 million compared to 1.7 million in 2.0.\\r\\n\\r\\nAnd it does a better job of identifying dates and fields.\\r\\n\\r\\nThe result is a richer and more accurate portrait of the curriculum of higher education. Is it perfect? No. Spend some time browsing the data and you will find errors. But it is bigger and better\u2013and we hope more useful and interesting to faculty, students, lifelong learners, and bibliophiles of all kinds."},{"id":"about-that-nobel-prize","metadata":{"permalink":"/about-that-nobel-prize","source":"@site/blog/2020-11-13-about-that-nobel-prize.md","title":"About that Nobel Prize\u2026","description":"Olga Togarczuk won the Nobel Prize in Literature in 2018\\\\. She appears on 22 syllabi in the OS dataset. Peter Handke won in 2019 and appears on 221\\\\. Louise Gl\xfcck, who won this past September, appears on 91\\\\. These are low numbers (even assuming, in Gl\xfcck\u2019s case, that we structurally undercount poetry, which we probably do). None of these authors are widely taught. Curious, I spent some time exploring the place of Nobel Prize-winners in the curriculum. The results are pretty striking. Here are the past forty Literature winners.","date":"2020-11-13T00:00:00.000Z","formattedDate":"November 13, 2020","tags":[{"label":"opensyllabus","permalink":"/tags/opensyllabus"}],"readingTime":3.1,"hasTruncateMarker":false,"authors":[{"name":"Joe Karaganis","url":"https://github.com/karaganis","imageURL":"https://avatars.githubusercontent.com/u/5721537?v=4","key":"joe"}],"frontMatter":{"slug":"about-that-nobel-prize","title":"About that Nobel Prize\u2026","authors":["joe"],"tags":["opensyllabus"]},"prevItem":{"title":"Welcome to Open Syllabus 2.5","permalink":"/welcome-to-open-syllabus-2-5"},"nextItem":{"title":"Your Favorite Episode","permalink":"/your-favorite-episode"}},"content":"Olga Togarczuk won the Nobel Prize in Literature in 2018\\\\. She appears on 22 syllabi in the OS dataset. Peter Handke won in 2019 and appears on 221\\\\. Louise Gl\xfcck, who w
1on this past September, appears on 91\\\\. These are low numbers (even assuming, in Gl\xfcck\u2019s case, that we structurally undercount poetry, which we probably do). None of these authors are widely taught. Curious, I spent some time exploring the place of Nobel Prize-winners in the curriculum. The results are pretty striking. Here are the past forty Literature winners.\\r\\n\\r\\n![](./img/nobel-literature-winners-taught.webp)\\r\\n\\r\\nI\u2019ve struggled somewhat to make generalizations here. There is clearly a lot variation in how often the prize winners are taught, ranging from the ubiquitous Toni Morrison to a whole raft of writers who are almost never assigned. The notions of literary reputation and value that animate the Nobel committee appear to have little connection to the judgements that faculty make in assigning texts. Nor\u2013by all appearances\u2013is winning a prize a guarantee of more teaching attention.\\r\\n\\r\\nFor Nobel watchers, this probably isn\u2019t a surprise. Generations of commentators have written about the byzantine politics of literary reputation and influence that shape the prize, about its varieties of regional and gender bias and more recent politics of outreach, and about the diverse uses of the prize for social and political commentary. There are endless arguments about the uneven judgement of the committee, focused mostly on the literary giants left unrecognized and [the winners who were (and remain) obscure](https://www.nobelprize.org/prizes/lists/all-nobel-prizes-in-literature/). (For an entertaining, polemical summary, [see Myer 2007](https://www.jstor.org/stable/40284378?&seq=1)).\\r\\n\\r\\nThese factors surely play some role in the different teaching fates of the winners, but also lead into a thicket of subjective critique. We can perhaps tease out a couple simpler patterns.\\r\\n\\r\\nPutting aside Morrison\u2019s outlier popularity for a moment, the Anglo-American winners\u2013Ishiguro, Munro, Lessing, Pinter, Heaney, Golding\u2013are pretty well and consistently represented in teaching. Counts between 1000 and 2000 put them in the company of canonical writers like Livy (1800 appearances), Carlyle, (1850), Proust (1594), and Alcott (1612) though not among the highest-scorers that all literature majors and many other students will encounter at least once in their studies.\\r\\n\\r\\nNon-British European writers, in contrast, account for a lot of Nobel Prizes but are nearly invisible in the curriculum. Only Gunter Grass (the 1998 winner) cracks 500 appearances, and his most assigned title, _The Tin Drum_, appears only 211 times. None of the other continental European winners\u2013Modiano, Transtr\xf6mer, M\xfcller, Le Cl\xe9zio, Jelinek, Saramago, Fo, Cela, Simon, Seifert, and so on\u2013are likely to be encountered outside the rare regional or country-focused literature class. The two Chinese winners are also rarely taught.\\r\\n\\r\\nOne simple explanation is that the Nobel Prize committee works with a concept of \u2018world\u2019 literary culture that has no equivalent in university teaching. Most teaching continues to pass through national traditions, which in our mostly US-based sample favors British and American writers and pushes the study of most other literatures to the edges of the curriculum.\\r\\n\\r\\nThere is, nonetheless, a strong framework for cross-cultural literary comparison in the Nobel results. The ascendancy of post-colonial literary studies is visible throughout, from Coetzee\u2019s 3675 appearances, to Naipaul\u2019s 1375 and Walcott\u2019s 1353; from Vargas Llosa\u2019s 1073 to Paz\u2019s 1012, Soyinka\u2019s 1563, and Garcia-Marquez\u2019s 2342\\\\. And arguably through Morrison\u2019s 10,414\\\\. The most visible division in the results is between authors who fit easily within a successfully-institutionalized post-colonial teaching enterprise and those (mostly continental European authors) who don\u2019t.\\r\\n\\r\\nObviously a handful of prizes provide a limited view of this topic. But there is the beginning of a story here about competing concepts of literary value, the
1ir different forms of institutionalization (in the case of post colonial studies across thousands of classes), and the zero-sum nature of the choices they create."},{"id":"your-favorite-episode","metadata":{"permalink":"/your-favorite-episode","source":"@site/blog/2020-10-30-your-favorite-episode.md","title":"Your Favorite Episode","description":"Recently we\u2019ve been exploring the place of \u2018non-traditional\u2019 materials in the curriculum: newspaper and magazine articles, TV and radio episodes, podcasts, blogs, and so on. Such materials are, of course, both very common on syllabi and largely invisible to traditional approaches to curricular design. They have been invisible in Open Syllabus, too, which relies on library catalogs to describe the range of titles that we can search for in the syllabus collection. Over the summer we decided to address this by extracting URLs in the collection, walking them back to their sources, and filtering for work used in instructional contexts. We now have a very interesting catalog of non-traditional classroom materials.","date":"2020-10-30T00:00:00.000Z","formattedDate":"October 30, 2020","tags":[{"label":"opensyllabus","permalink":"/tags/opensyllabus"}],"readingTime":1.795,"hasTruncateMarker":false,"authors":[{"name":"Joe Karaganis","url":"https://github.com/karaganis","imageURL":"https://avatars.githubusercontent.com/u/5721537?v=4","key":"joe"}],"frontMatter":{"slug":"your-favorite-episode","title":"Your Favorite Episode","authors":["joe"],"tags":["opensyllabus"]},"prevItem":{"title":"About that Nobel Prize\u2026","permalink":"/about-that-nobel-prize"},"nextItem":{"title":"How Much Traction do Open Access / Open Educational Resources have in the Classroom?","permalink":"/how-much-traction-do-open-access-open-educational-resources-have-in-the-classroom"}},"content":"Recently we\u2019ve been exploring the place of \u2018non-traditional\u2019 materials in the curriculum: newspaper and magazine articles, TV and radio episodes, podcasts, blogs, and so on. Such materials are, of course, both very common on syllabi and largely invisible to traditional approaches to curricular design. They have been invisible in Open Syllabus, too, which relies on library catalogs to describe the range of titles that we can search for in the syllabus collection. Over the summer we decided to address this by extracting URLs in the collection, walking them back to their sources, and filtering for work used in instructional contexts. We now have a very interesting catalog of non-traditional classroom materials.\\r\\n\\r\\nThis is a big topic that will probably be the subject of several posts, but let\u2019s look at some fun stuff first: TV/radio shows and podcasts that do serious long-form exploration of topics. Many of these are assigned with some frequency on syllabi \u2014 though we suspect more often as supplements to conventional assignments than as primary materials. It would be interesting to test this via a deeper dive into the documents. In any case, there is some standout programming that has a strong presence in the classroom. For example, Frontline:\\r\\n\\r\\n![](./img/frontline-episodes.webp)\\r\\n\\r\\n\u201c[Spying on the Home Front](https://www.pbs.org/wgbh/pages/frontline/homefront/),\u201d \u201c[On Our Watch](https://www.pbs.org/wgbh/pages/frontline/darfur/)\u201d (which is about the genocide in Darfur), and \u201c[Ghosts of Rwanda](https://www.pbs.org/wgbh/pages/frontline/shows/ghosts/video/)\u201d play central roles in the teaching of their respective topics. For comparison, the other major Rwanda titles \u2014 Prunier\u2019s _The Rwanda Crisis_, Powers\u2019 _Bystanders to Genocide_, and Kuperman\u2019s _Rwanda in Retrospect_\u2013are assigned 290, 237, and 201 times respectively.\\r\\n\\r\\n\u201cThis American Life\u201d episodes are also widely assigned. \u2018[The Giant Pool of Money](https://www.thisamericanlife.org/355/the-giant-pool-of-money)\u2018 won a Peabody award and plays a significant role in teaching the financial crisis. \u2018[The Problem We all Live With](https://www.thisamericanlife.org/562/the-problem-we-all-live-with-part-one)\u2018 is about the fate of school desegregation.\\r\\n\\r\\n![](./img/am-life-episodes.webp)\\r\\n\\r\\nLastWeekTonight has some frequently assigned episodes\u2013notably one on [bad journalistic coverage of scientific studies](https://www.youtube.com/watch?v=0Rnq1NpHdmw).\\r\\n\\r\\n![](./img/last-week-tonight-episodes.webp)\\r\\n\\r\\nRadiolab has one strong performer: an episode on the mind-body relationship called [Where Am I](https://www.wnycstudios.org/podcasts/radiolab/episodes/91524-where-am-i).\\r\\n\\r\\n![](./img/radiolab-episodes.webp)\\r\\n\\r\\nThere is more to explore and it will be a while, yet, before this data is integrated into the larger OS dataset. Next stop: magazines and newspapers."},{"id":"how-much-traction-do-open-access-open-educational-resources-have-in-the-classroom","metadata":{"permalink":"/how-much-traction-do-open-access-open-educational-resources-have-in-the-classroom","source":"@site/blog/2020-10-06-how-much-traction-do-open-access-open-educational-resources-have-in-the-classroom.md","title":"How Much Traction do Open Access / Open Educational Resources have in the Classroom?","description":"Open Access (OA) monographs and Open Educational Resource (OER) textbooks are works that are \u2018openly licensed\u2019 \u2014 that is, they can be used and distributed for free. In a world of $200 textbooks, OA/OER plays a fairly high-profile role in efforts to reduce the cost of education.","date":"2020-10-06T00:00:00.000Z","formattedDate":"October 6, 2020","tags":[{"label":"opensyllabus","permalink":"/tags/opensyllabus"}],"readingTime":2.3,"hasTruncateMarker":false,"authors":[{"name":"Joe Karaganis","url":"https://github.com/karaganis","imageURL":"https://avatars.githubusercontent.com/u/5721537?v=4","key":"joe"}],"frontMatter":{"slug":"how-much-traction-do-open-access-open-educational-resources-have-in-the-classroom","title":"How Much Traction do Open Access / Open Educational Resources have in the Classroom?","authors":["joe"],"tags":["opensyllabus"]},"prevItem":{"title":"Your Favorite Episode","permalink":"/your-favorite-episode"},"nextItem":{"title":"Open Syllabus 2.0","permalink":"/open-syllabus-2-0"}},"content":"Open Access (OA) monographs and Open Educational Resource (OER) textbooks are works that are \u2018openly licensed\u2019 \u2014 that is, they can be used and distributed for free. In a world of $200 textbooks, OA/OER plays a fairly high-profile role in efforts to reduce the cost of education.\\r\\n\\r\\nBut free circulation makes it difficult to track classroom adoption, which in turn makes it difficulty to understand the shape of demand for OA/OER work\u2013either overall or with respect to particular subjects. The link between supply and demand established in the commercial book market by a sale doesn\u2019t exist in the OA/OER world. Our thought is that this delinking is one reason\u2013and maybe a significant reason\u2013for the relatively low rate of adoption of OA/OER in teaching, despite over a decade of efforts. It\u2019s still too hard to characterize demand for these titles to faculty, curricular designers, publishers, and investors. It\u2019s hard to tell what\u2019s popular and what\u2019s been effectively adopted in peer institutions.\\r\\n\\r\\nSo we\u2019re eager to see what happens when we partially close this information loop by measuring demand via syllabi. Here\u2019s a normalized US trendline for OA/OER adoption based on the OS collection (drawing on catalog information from the [Open Textbook Library](https://
1open.umn.edu/opentextbooks) and the [Directory of Open Access Books](https://www.doabooks.org/)). It shows rapid OER textbook growth in recent years\u2013but from a very low baseline. In 2017, roughly 1 in 300 classes used OER textbooks and around 1 in 400 assigned an OA monograph (the lighter blue is for textbooks; darker for monographs).\\r\\n\\r\\n![](./img/normalized-citations-counts-us.webp)\\r\\n\\r\\nThat\u2019s across all \u2018US syllabi with citations,\u2019 which requires caveats since some types of classes don\u2019t have assigned materials.\\r\\n\\r\\nThe availability of good OER materials also varies significantly by field and topic. Studies that have focused narrowly on classes for which there are good OER equivalents find significantly higher rates of adoption\u2013[around 6% in this 2018 study](https://opencontent.org/blog/archives/5820). Recent [surveys of OER use](https://www.onlinelearningsurvey.com/oer.html) also suggest higher and rising numbers. We can\u2019t zero in on specific classes but we can explore differences between fields. In Math\u2013a field with a number of widely used OER textbooks\u2013we put US adoption at 1.5% of \u2018syllabi with citations\u2019 in 2017 (and climbing).\\r\\n\\r\\n![](./img/normalized-citations-counts-math-us.webp)\\r\\n\\r\\nThere are other ways to slice this data. The chart below focuses on US 2-year colleges. The data is choppier (we may drop the early 2000s from charts, which are noisy when normalized) but pretty clearly show sharp recent growth in textbook adoption\u2013and virtually no role for OA monographs, which are typically more advanced scholarly work.\\r\\n\\r\\n![](./img/normalized-citations-counts-two-year-us.webp)\\r\\n\\r\\nThere is a decent case, in other words, that OER is at a takeoff point in higher ed\u2013though still at very low levels of adoption across the curriculum. These charts come from a dashboard that we\u2019ll publish in a couple weeks. Then you can explore."},{"id":"open-syllabus-2-0","metadata":{"permalink":"/open-syllabus-2-0","source":"@site/blog/2019-06-16-open-syllabus-2-0.md","title":"Open Syllabus 2.0","description":"Welcome to the Open Syllabus Project 2.0\\\\. Now you can explore college teaching, publishing, and intellectual traditions across 6 million classes, 4700 schools, and 79 countries.","date":"2019-06-16T00:00:00.000Z","formattedDate":"June 16, 2019","tags":[{"label":"opensyllabus","permalink":"/tags/opensyllabus"}],"readingTime":0.335,"hasTruncateMarker":false,"authors":[{"name":"Joe Karaganis","url":"https://github.com/karaganis","imageURL":"https://avatars.githubusercontent.com/u/5721537?v=4","key":"joe"}],"frontMatter":{"slug":"open-syllabus-2-0","title":"Open Syllabus 2.0","authors":["joe"],"tags":["opensyllabus"]},"prevItem":{"title":"How Much Traction do Open Access / Open Educational Resources have in the Classroom?","permalink":"/how-much-traction-do-open-access-open-educational-resources-have-in-the-classroom"},"nextItem":{"title":"Syllabus of the Month (January)","permalink":"/syllabus-of-the-month-january-2"}},"content":"Welcome to the Open Syllabus Project 2.0\\\\. Now you can explore college teaching, publishing, and intellectual traditions across 6 million classes, 4700 schools, and 79 countries.\\r\\n\\r\\nYou can dive into schools and fields, look at how the adoption of texts changes over time, and compare how teaching varies in different countries.\\r\\n\\r\\nSo [explore](https://opensyllabus.org/), let us know what you think, and give some thought to [sharing your syllabi.](http://blog.opensyllabus.org/share-syllabi/)"},{"id":"syllabus-of-the-month-january-2","metadata":{"permalink":"/syllabus-of-the-month-january-2","source":"@site/blog/2019-01-16-syllabus-of-the-month-january-2.md","title":"Syllabus of the Month (January)","description":"We received this 1996 Wesleyan University syllabus as a remembrance of the instructor by a former student. It\u2019s January\u2019s syllabus of the month.","date":"2019-01-16T00:00:00.000Z","formattedDate":"January 16, 2019","tags":[{"label":"opensyllabus","permalink":"/tags/opensyllabus"}],"readingTime":3.795,"hasTruncateMarker":false,"authors":[{"name":"Joe Karaganis","url":"https://github.com/karaganis","imageURL":"https://avatars.githubusercontent.com/u/5721537?v=4","key":"joe"}
1],"frontMatter":{"slug":"syllabus-of-the-month-january-2","title":"Syllabus of the Month (January)","authors":["joe"],"tags":["opensyllabus"]},"prevItem":{"title":"Open Syllabus 2.0","permalink":"/open-syllabus-2-0"},"nextItem":{"title":"Big in Japan","permalink":"/big-in-japan"}},"content":"We received this 1996 Wesleyan University syllabus as a remembrance of the instructor by a former student. It\u2019s January\u2019s syllabus of the month.\\r\\n\\r\\n> Fall 1996  \\r\\n> Col/Hum 104\\r\\n> \\r\\n> **Teachers and Their Teachings: From Socrates to Foucault**\\r\\n> \\r\\n> Howard Bernstein  \\r\\n> X 2323 Butterfield C313, Office hours, T, Th 4-5 and by Appt\\r\\n> \\r\\n> General description: This course is about teachers and students, their relationships, and some powerful pedagogical ideas; it is also about maturation and longing, power and subordination, deception and self-deception, transference and counter-transference. We will be asking questions about what it means to \u201ceducate,\u201d to take responsibility for the shaping of another soul, to transmit culture, to confront and to provoke, and perhaps, also, to insinuate, manipulate, and judge. What is it that students alternatively crave and fear in the educative process? Why do teachers presume to teach when the opportunities to do otherwise are often more glitteringly attractive? Our task is to examine different, and sometimes discordant, models of teaching and learning from classical antiquity virtually to the present. As little as possible will be presupposed or assumed to be self-evident, including the almost sacrosanct notion that \u201ceducation\u201d (liberal or illiberal) is a good thing for which there is some sort of intrinsic \u201cneed.\u201d\\r\\n\\r\\n> Major readings in order of appearance (mostly):\\r\\n\\r\\n> David Mamet, _Oleanna_\\r\\n> \\r\\n> Allan Bloom, _The Closing of the American Mind_\\r\\n> \\r\\n> Plato, the Republic, _Apology_\\r\\n> \\r\\n> Selections from the _Synoptic Gospels_, and the _Pauline Epistles_.\\r\\n> \\r\\n> _Letters of Abelard and Heloise_\\r\\n> \\r\\n> Jean-Jacques Rousseau, _Emile_\\r\\n> \\r\\n> Michel Foucault, _Discipline and Punish_\\r\\n> \\r\\n> John Holt, _Freedom and Beyond_\\r\\n\\r\\n\\r\\n* * *\\r\\n\\r\\n> (Note: there will also be short supplementary readings bound together in a packet.)\\r\\n> \\r\\n> **Course Calendar**\\r\\n> \\r\\n> 5 September: Introductions and housekeeping. What does a \u201cteacher \u201d do?\\r\\n> \\r\\n> 10 Sept: **READ:** David Mamet, Oleanna.\\r\\n> \\r\\n> 12, 17 Sept: **READ:** Allan Bloom, The Closing of the American Mind pp. 19-23, 25- 43, 62-7, 243-5, 264(top)-284, 380-2\\\\. (Here we are especially interested in Bloom\u2019s evocation of the Socratic image in the cause of educational traditionalism. Bloom can be infuriating, but that may well be the point, so be patient).\\r\\n> \\r\\n> 19, 24, 26 Sept: **READ:** Plato, Republic, Books III, IV, VI , VII, IX. (Note: Bk VII contains the famous \u201cSim
1ile of the Cave\u201d and Plato/Socrates\u2019s vivid representation of the educative process as the \u201cturning around\u201d of the soul.\\r\\n> \\r\\n> 1 Oct: **READ:** Plato, _Apology_ (The Last Days of Socrates) in Laser Packet of course readings.\\r\\n> \\r\\n> 8 Oct: **READ:** Biblical Selections in course reader. Read these in order beginning with the Gospel according to Saint Matthew. (Our special interest here is in Jesus as charismatic teacher and also, inevitably, in the message).\\r\\n\\r\\n> Don\u2019t forget the Pheme Perkins stuff in Read as well.\\r\\n> \\r\\n> 10 Oct: **READ:** _Letters of Abelard and Heloise_, pp. 9-106.\\r\\n> \\r\\n> 15 Oct: **READ;** Letters, pp. 109-179.\\r\\n> \\r\\n> 17, 22 Oct: **READ:** Jean-Jacques Rousseau, Emile, Book I, II\\r\\n> \\r\\n> **_22\xa0 \xa0_October\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0 Mid-Term Due In Class**\\r\\n> \\r\\n> 29 Oct., 5 Nov: **READ** Rousseau, Bk III, IV\xa0\xa0 (You may omit pp. 266-313 which happens to be the celebrated \u201cConfession of Faith of a Savoyard Vicar\u201d)\\r\\n> \\r\\n> 7, 12 Nov:\xa0 **READ:** Michel Foucault, _Discipline and Punish_, pp. 3-72, 135-194\\\\. There is also introductory material in the Sir Speedy packet under the heading The Foucault Reader. (Note: You may well be wondering at the outset what all this stuff on torture and punishment has to do with teaching and education. Believe me, the connection will clarify itself in time).\\r\\n> \\r\\n> 14, 19, 21 Nov: **READ:** Foucault, pp. 195-end, and then, for contrast, Bloom, pp. 47-61, 68-137 , 313-356.\\r\\n> \\r\\n> 26 Nov. 3 December: **READ:** Holt, _Freedom and Beyond_.\\r\\n\\r\\n> 5 Dec: David Mamet\u2019s Movie version of Oleanna\\r\\n\\r\\n> 10 Dec: Review and retrospective.\\r\\n\\r\\n> FINAL PAPER DUE IN MY OFFICE\\r\\n\\r\\n> **12 DECEMBER** Housekeeping : There are two formal papers required for this course, one at mid\xad term and the other as a final project. These will be approximately 6-8 typed pages in length. Students are also required to keep and submit a weekly journal about which I will have more to say in class. Participation in a discussion course such as this is also important. From to time, there will be informal lectures, but without any set pattern, and there will always be occasion for free for all question and answer sessions"},{"id":"big-in-japan","metadata":{"permalink":"/big-in-japan","source":"@site/blog/2018-10-08-big-in-japan.md","title":"Big in Japan","description":"We always like to learn more about how the OSP is being used, and sometimes this yields an unexpected treat.\xa0 Here\u2019s a short segment from a Japanese quiz show called \u2018Wow! Surprising Japan,\u2019 in which foreigners are quizzed about their knowledge of Japan.\xa0 The segment discusses novelist Souseki Natsume, who comes in 11th in some sort of power ranking of famous Japanese people.\xa0 Here, the OSP is used as an authority for what foreigners might know about Japan.\xa0 According to our data, Natsume\u2019s Kokoro is assigned with some frequency.\xa0 Thanks, Daisuke!","date":"2018-10-08T00:00:00.000Z","formattedDate":"October 8, 2018","tags":[{"label":"opensyllabus","permalink":"/tags/opensyllabus"}],"readingTime":0.46,"hasTruncateMarker":false,"authors":[{"name":"Joe Karaganis","url":"https://github.com/karaganis","imageURL":"https://avatars.githubusercontent.com/u/5721537?v=4","key":"joe"}],"frontMatter":{"slug":"big-in-japan","title":"Big in Japan","authors":["joe"],"tags":["opensyllabus"]},"prevItem":{"title":"Syllabus of the Month (January)","permalink":"/syllabus-of-the-month-january-2"},"nextItem":{"title":"Some Preliminary Description of the V2 Dataset","permalink":"/some-preliminary-description-of-the-v2-dataset"}},"content":"We always like to learn more about how the OSP is being used, and sometimes this yields an unexpected treat.\xa0 Here\u2019s a short segment from a Japanese quiz show called \u2018Wow! Surprising Japan,\u2019 in which foreigners are quizzed about their knowledge of Japan.\xa0 The segment discusses novelist Souseki Natsume, who comes in 11th in some sort of power ranking of famous Japanese people.\xa0 Here, the OSP is used as an authority for what foreigners might know about Japan.\xa0 According to our data, Natsume\u2019s Kokoro is assigned with some frequency.\xa0 Thanks, Daisuke!\\r\\n\\r\\n[http://blog.opensyllabus.org/wp-content/uploads/2018/10/Wow-Surprising-Japan.mp4](http://blog.opensyllabus.org/wp-content/uploads/2018/10/Wow-Surprising-Japan.mp4)"},{"id":"some-preliminary-description-of-the-v2-dataset","metadata":{"permalink":"/some-preliminary-description-of-the-v2-dataset","source":"@site/blog/2018-05-22-some-preliminary-description-of-the-v2-dataset.md","title":"Some Preliminary Description of the V2 Dataset","description":"The next version of the OSP dataset is beginning to take shape.\xa0 It will have roughly 6 million syllabi, covering around 6000 institutions around the world.\xa0 All the stats and breakdowns will bounce around as we refine the classifiers, decide which fields to consolidate, and so on, but here are a couple interesting initial views:","date":"2018-05-22T00:00:00.000Z","formattedDate":"May 22, 2018","tags":[{"label":"opensyllabus","permalink":"/tags/opensyllabus"}],"readingTime":0.31,"hasTruncateMarker":false,"authors":[{"name":"Joe Karaganis","url":"https://github.com/karaganis","imageURL":"https://avatars.githubusercontent.com/u/5721537?v=4","key":"joe"}],"frontMatter":{"slug":"some-preliminary-description-of-the-v2-dataset","title":"Some Preliminary Description of the V2 Dataset","authors":["joe"],"tags":["opensyllabus"]},"prevItem":{"title":"Big in Japan","permalink":"/big-in-japan"},"nextItem":{"title":"Syllabus of the Month (February)","permalink":"/syllabus-of-the-month-february-2"}},"content":"The next version of the OSP dataset is beginning to take shape.\xa0 It will have roughly 6 million syllabi, covering around 6000 institutions around the world.\xa0 All the stats and breakdowns will bounce around as we refine the classifiers, decide which fields to consolidate, and so on, but here are a couple interesting initial views:\\r\\n\\r\\n![](./img/syllabi-by-year.webp)\\r\\n\\r\\nAnd a breakdown by field:\\r\\n\\r\\n![](./img/fields-2.webp)"},{"id":"syllabus-of-the-month-february-2","metadata":{"permalink":"/syllabus-of-the-month-february-2","source":"@site/blog/2018-02-16-syllabus-of-the-month-february-2.md","title":"Syllabus of the Month (February)","description":"We\u2019re on a melancholic run for the winter months.\xa0 February\u2019s honoree is \u201cThe Professor of Longing\u201d by Jill Talbot.","date":"2018-02-16T00:00:00.000Z","formattedDate":"February 16, 2018","tags":[{"label":"opensyllabus","permalink":"/tags/opensyllabus"}],"readingTime":4.16,"hasTruncateMarker":false,"authors":[{"name":"Joe Karaganis","url":"https://github.com/karaganis","imageURL":"https://avatars.githubusercontent.com/u/5721537?v=4","key":"joe"}
1],"frontMatter":{"slug":"syllabus-of-the-month-february-2","title":"Syllabus of the Month (February)","authors":["joe"],"tags":["opensyllabus"]},"prevItem":{"title":"Some Preliminary Description of the V2 Dataset","permalink":"/some-preliminary-description-of-the-v2-dataset"},"nextItem":{"title":"Syllabus of the Month (January)","permalink":"/syllabus-of-the-month-january"}},"content":"We\u2019re on a melancholic run for the winter months.\xa0 February\u2019s honoree is [\u201cThe Professor of Longing\u201d by Jill Talbot](http://www.thediagram.com/13_3/talbot.html).\\r\\n\\r\\n> **243: The Professor of Longing**\\r\\n> \\r\\n> **Dr. Jill Talbot**  \\r\\n> **Contact:**\[email protected] | 426-7060  \\r\\n> **Office:**\xa0LA 102 C (a room I share with a broken shelf and three people I never see)  \\r\\n> **Office Hours:**\xa0Before and After Class and once in a booth in the Hyde Park Bar & Grill\\r\\n> \\r\\n> **Course** **Description:** This course is about failed attempts. It\u2019s about me standing in an office two states and two months ago handing over a letter declaring that I was leaving academia indefinitely. It\u2019s about being on the road\u2014Utah, Idaho, Montana\u2014climbing north before having to turn around, scramble south. It\u2019s about the trying months of summer and ending up in a circumstance not on any map. It\u2019s about Boise instead of Missoula, about adjustments instead of adventure, about impediments edging out impulse, bi-monthly paychecks that can\u2019t cover rent and daycare, my last cigarette. It will be writing in a cramped corner on a plastic tv tray in a foldout chair bought at a thrift store. By the end of the semester, the focus will be two am phone calls and bad checks. For the final, look for a bookcase and a loveseat in a living room with the front door left wide open, my four-year-old daughter\u2019s favorite polka-dotted vest forgotten on the kitchen counter.\\r\\n> \\r\\n> **Texts:**\xa0We\u2019re not going to read anything beyond my own proclivities. We\u2019ll discuss stories, essays, and poems that remind me of my most recent misgivings, the lingerings I\u2019m unable to yield, the words underlining my past. Our study will include recurring images, my own, of course, as well as the themes of my disposition. The text in this class is me.\\r\\n> \\r\\n> **Attendance:** It\u2019s strange to think I\u2019m even here. Years from now, I will feel as these weeks were nothing more than an interruption, a curve in the story\u2019s road.\\r\\n> \\r\\n> **Disclaimer:**\xa0While these aren\u2019t the texts I really used that semester, they most accurately reflect who I was during those weeks when I kept my eyes to the sidewalk.  \\r\\n> \\r\\n> <table border=\\"0\\" width=\\"100%\\" cellspacing=\\"5\\" cellpadding=\\"5\\">\\r\\n> \\r\\n> <tbody>\\r\\n> \\r\\n> <tr>\\r\\n> \\r\\n> <td className=\\"bodyblack\\" align=\\"right\\" valign=\\"top\\">\xa0\\r\\n> \\r\\n> **August\xa022**\\r\\n> \\r\\n> </td>\\r\\n> \\r\\n> <td className=\\"bodyblack\\" align=\\"left\\" valign=\\"top\\">\xa0\xa0\xa0\xa0\xa0</td>\\r\\n> \\r\\n> <td className=\\"bodyblack\\" align=\\"left\\" valign=\\"top\\">\\r\\n> \\r\\n> Walt Whitman and Emily Dickinson, Selections\\r\\n> \\r\\n> Whitman has many famous lines about celebrating himself and containing multitudes and taking to the open road, sounding his barbaric yawp, yet stylistically, he used a device called \u201ccataloging.\u201d A long list. Write that down. It\u2019s important, because we all catalog, make long lists of lovers, of things to pack, pros and cons, items at the drugstore. Some catalogs come with details, like wine lists. Some in a shorthand no one but us can read, and if enough time goes by, neither can we, as we pull a forgotten slip of paper from the bottom of a purse or a pocket and stare at a mystery.\\r\\n> \\r\\n> Dickinson used dashes in her lines, random capitalization, difficult to decipher punctuation. She wasn\u2019t consistent in her usage, and often her poems were in unfinished forms. But it\u2019s the dashes that draw me, so we\u2019ll focus on those. Sometimes they appear at the end of a line, others in the middle, interruptions. Still, other poems are words alone, no dashes at all. Emphasis? A writer\u2019s pen carrying over to the next word, down the line? Never intended as part of the prosody at all, like a pause in a conversation misinterpreted as silence or disagreement when it\u2019s only search for the right words? Or are they like bridges crossing a question?\\r\\n> \\r\\n> We\u2019ll be seeing these elements throughout the semester: catalogs of loss, of what lies between or is left to the end, the choices too difficult to decipher.\\r\\n> \\r\\n> I\u2019ll tell you up front: he left. So let\u2019s look at an opening line of Dickinson\u2019s: \u201cYou left me, sweet, two legacies\u2014\u201d  \\r\\n> And he did, one, the legacy of our years together that began with the Eagle River and a half moon. The other, the sweetest legacy, our daughter, who, I suppose, he never saw as part of his prosody. That dash\u2014his disappearance. And so, to the Whitmanesque open road he went, \u201cafoot and lighthearted,\u201d while, me? My lines are a bit further down: \u201cI carry my old delicious burdens . . . . I carry them with me where I go/I swear it is impossible for me to get rid of them.\u201d\\r\\n> \\r\\n> The delicious burdens I bear because no state, not this one or the last three I\u2019ve lived in can trace a line underneath Kenny and make him pay child support. He\u2019s the dash that keeps dashing, a catalog of unanswered phone calls.\\r\\n> \\r\\n> </td>\\r\\n> \\r\\n> </tr>\\r\\n> \\r\\n> </tbody>\\r\\n> \\r\\n> </table>\\r\\n> \\r\\n> The rest is at [Diagram](http://www.thediagram.com/13_3/talbot.html)."},{"id":"syllabus-of-the-month-january","metadata":{"permalink":"/syllabus-of-the-month-january","source":"@site/blog/2018-01-24-syllabus-of-the-month-january.md","title":"Syllabus of the Month (January)","description":"This month\u2019s honoree is:","date":"2018-01-24T00:00:00.000Z","formattedDate":"January 24, 2018","tags":[{"label":"opensyllabus","permalink":"/tags/opensyllabus"}],"readingTime":1.53,"hasTruncateMarker":false,"authors":[{"name":"Joe Karaganis","url":"https://github.com/karaganis","imageURL":"https://avatars.githubusercontent.com/u/5721537?v=4","key":"joe"}],"frontMatter":{"slug":"syllabus-of-the-month-january","title":"Syllabus of the Month (January)","authors":["joe"],"tags":["opensyllabus"]},"prevItem":{"title":"Syllabus of the Month (February)","permalink":"/syllabus-of-the-month-february-2"},"nextItem":{"title":"OSP wins a Digital Science \u2018Catalyst Grant\u2019","permalink":"/osp-wins-a-digital-science-catalyst-grant"}},"content":"This month\u2019s honoree is:\\r\\n\\r\\n> # PHL289: THE PHILOSOPHY OF ADJUNCTING\\r\\n> \\r\\n> Instructor: Kevin Temple  \\r\\n> Office hours: By text message\\r\\n> \\r\\n> ## Course Description\\r\\n> \\r\\n> There is no such thing as the Philosophy of Adjuncting; but rest assured, this course is authentic, for I am being deliriously underpaid to teach it. As the \u201cinstru
1ctor of record,\u201d I have made the syllabus distinctly my own because that tiny gasp of freedom is to tenure what adjunct pay is to an actual salary. What have I put on it? Nothing of use. It is self-defeating, for that is what a philosophy of adjuncting must be.\\r\\n> \\r\\n> 1.  THE STRUCTURAL PROBLEM\\r\\n> \\r\\n> Week 1: Marx on alienation\\r\\n> \\r\\n> My adjunct friend says, \u201cThe irony of adjuncting is being alienated labor while teaching future alienated laborers about Alienated Labor.\u201d Read the Alienated Labor section of Marx\u2019s \u201cEconomic-Philosophic Manuscripts.\u201d Alienation happens in a bunch of ways; for example, when instead of doing something great on your own terms, an arbitrarily powerful person forces you to do it his way. He ruins what you do by breaking it down into a series of distinct tasks, automating whatever can be automated, measuring how long each remaining task takes, and then paying you as little as possible per task. That\u2019s how administrators created adjuncting. It\u2019s almost like they\u2019ve read Marx.\\r\\n> \\r\\n> Week 2: Adorno saw it coming\\r\\n> \\r\\n> We discuss the \u201cculture industry.\u201d Universities as a whole have what Adorno called a \u201cculture monopoly.\u201d As such, he says, \u201cThey cannot afford to neglect their appeasement of the real holders of power if their sphere of activity in mass society \u2026 is not to undergo a series of purges.\u201d Well, guess what? The purges happened anyway. This is why we commute.\\r\\n\\r\\n[Continued at _Adjunct Commuter Weekly\u2026_](https://adjunctcommuterweekly.com/2015/10/23/syllabus-the-philosophy-of-adjuncting-by-kevin-temple/)"},{"id":"osp-wins-a-digital-science-catalyst-grant","metadata":{"permalink":"/osp-wins-a-digital-science-catalyst-grant","source":"@site/blog/2017-09-19-osp-wins-a-digital-science-catalyst-grant.md","title":"OSP wins a Digital Science \u2018Catalyst Grant\u2019","description":"We\u2019re pleased to announce that we\u2019ve won a Catalyst Grant for innovative research tool-building projects, offered by Digital Science.\xa0 The grant will support the extension of the OSP into other languages, beginning with German, Spanish, and Jap
1anese (languages in which we have large document collections).","date":"2017-09-19T00:00:00.000Z","formattedDate":"September 19, 2017","tags":[{"label":"opensyllabus","permalink":"/tags/opensyllabus"}],"readingTime":0.255,"hasTruncateMarker":false,"authors":[{"name":"Joe Karaganis","url":"https://github.com/karaganis","imageURL":"https://avatars.githubusercontent.com/u/5721537?v=4","key":"joe"}],"frontMatter":{"slug":"osp-wins-a-digital-science-catalyst-grant","title":"OSP wins a Digital Science \u2018Catalyst Grant\u2019","authors":["joe"],"tags":["opensyllabus"]},"prevItem":{"title":"Syllabus of the Month (January)","permalink":"/syllabus-of-the-month-january"},"nextItem":{"title":"Syllabus of the Month (June)","permalink":"/syllabus-of-the-month-june"}},"content":"We\u2019re pleased to announce that we\u2019ve won a Catalyst Grant for innovative research tool-building projects, offered by [Digital Science](https://www.digital-science.com/).\xa0 The grant will support the extension of the OSP into other languages, beginning with German, Spanish, and Jap
1anese (languages in which we have large document collections).\xa0\\r\\n\\r\\n[More info on the Catalyst Grants.](https://www.digital-science.com/blog/news/three-innovative-software-tools-boosted-digital-sciences-catalyst-grant/)"},{"id":"syllabus-of-the-month-june","metadata":{"permalink":"/syllabus-of-the-month-june","source":"@site/blog/2017-05-29-syllabus-of-the-month-june.md","title":"Syllabus of the Month (June)","description":"This month\u2019s honorees are Carl Bergstrom and Jevin West from the University of Washington for their very ambitious INFO 198 / BIOL 106B course:","date":"2017-05-29T00:00:00.000Z","formattedDate":"May 29, 2017","tags":[{"label":"opensyllabus","permalink":"/tags/opensyllabus"}],"readingTime":0.815,"hasTruncateMarker":false,"authors":[{"name":"Joe Karaganis","url":"https://github.com/karaganis","imageURL":"https://avatars.githubusercontent.com/u/5721537?v=4","key":"joe"}],"frontMatter":{"slug":"syllabus-of-the-month-june","title":"Syllabus of the Month (June)","authors":["joe"],"tags":["opensyllabus"]},"prevItem":{"title":"OSP wins a Digital Science \u2018Catalyst Grant\u2019","permalink":"/osp-wins-a-digital-science-catalyst-grant"},"nextItem":{"title":"Syllabus of the Month (February)","permalink":"/syllabus-of-the-month-february"}},"content":"This month\u2019s honorees are Carl Bergstrom and Jevin West from the University of Washington for their very ambitious INFO 198 / BIOL 106B course:\\r\\n\\r\\n> ## Calling Bullshit in the Age of Big Data\\r\\n> \\r\\n> Our learning objectives are straightforward. After taking the course, you should be able to:\\r\\n> \\r\\n> *   Remain vigilant for bullshit contaminating your information diet.\\r\\n> *   Recognize said bullshit whenever and wherever you encounter it.\\r\\n> *   Figure out for yourself precisely _why_ a particular bit of bullshit is bullshit.\\r\\n> *   Provide a statistician or fellow scientist with a technical explanation of why a claim is bullshit.\\r\\n> *   Provide your crystals-and-[homeopathy](https://www.nhmrc.gov.au/_files_nhmrc/publications/attachments/cam02_nhmrc_statement_homeopathy.pdf) aunt or casually racist uncle with an accessible and persuasive explanation of why a claim is bullshit.\\r\\n> \\r\\n> We will be astonished if these skills do not turn out to be among the most useful and most broadly applicable of those that you acquire during the course of your college education.\\r\\n\\r\\n[More](http://callingbullshit.org/syllabus.html)."},{"id":"syllabus-of-the-month-february","metadata":{"permalink":"/syllabus-of-the-month-february","source":"@site/blog/2017-02-04-syllabus-of-the-month-february.md","title":"Syllabus of the Month (February)","description":"This month we highlight\xa0RISD\xa0Professor Clement Valla\u2019s course,\xa0Uncreative Design,\xa0and ponder its relationship to the OSP.","date":"2017-02-04T00:00:00.000Z","formattedDate":"February 4, 2017","tags":[{"label":"opensyllabus","permalink":"/tags/opensyllabus"}],"readingTime":1.275,"hasTruncateMarker":false,"authors":[{"name":"Joe Karaganis","url":"https://github.com/karaganis","imageURL":"https://avatars.githubusercontent.com/u/5721537?v=4","key":"joe"}],"frontMatter":{"slug":"syllabus-of-the-month-february","title":"Syllabus of the Month (February)","authors":["joe"],"tags":["opensyllabus"]},"prevItem":{"title":"Syllabus of the Month (June)","permalink":"/syllabus-of-the-month-june"},"nextItem":{"title":"Syllabus of the Month (September)","permalink":"/syllabus-of-the-month-september"}},"content":"This month we highlight\xa0RISD\xa0Professor Clement Valla\u2019s [course,\xa0_Uncreative Design_](http://udf13.cvalla.com/syllabus/),\xa0and ponder its relationship to the OSP.\\r\\n\\r\\n> ## DESCRIPTION\\r\\n> \\r\\n> \u201cIn 1969 the conceptual artist Douglas Heubler wrote, \u2018The world is full of objects, more or less interesting; I do not wish to add any more.\u2019\u201d So opens Kenneth Goldsmith\u2019s _Uncreative Writing_, published in 2011\\\\. Following Goldsmith\u2019s lead, this class will explore various strategies in art, writing and political activism that will lead us to an uncreative design. We will make use of copying, repetition, appropriation, detournement and bricolage in a series of studio experiments. Though the class will be focused on ways of (not)making, class participation and discussions of assigned readings will also play a major role in guiding studio work, and in evaluating student projects. There are no prerequisites, though students should be willing to take major risks and have a very open approach to different modes of working.\\r\\n> \\r\\n> Creativity, intuition, improvisation, composed, hand-made, unique, original, subjective, genius, authored. These are all to be avoided in this class. Rather what we create will be uncreative, systematic, scripted, chance based, calculated, mass produced, digital, appropriated, objective and copied. The role of contemporary producers is no longer be to create new things, but to channel, frame, re-assemble and contextualize existing things \u2013 from creative production towards an \u2018uncreative\u2019 production. Uncreative Design explores how new meaning is produced by collecting, archiving, captioning, erasing, parsing. There are many examples of this work and theory in other disciplines, including writing, art, new media, music, film and video."},{"id":"syllabus-of-the-month-september","metadata":{"permalink":"/syllabus-of-the-month-september","source":"@site/blog/2016-09-12-syllabus-of-the-month-september.md","title":"Syllabus of the Month (September)","description":"September\u2019s \u2018Syllabus of the Month\u2019 is a 1994 University of Chicago class, \u201cCurrent Issues in Racism and the Law,\u201d by a junior faculty member you may have heard of.\xa0 (Click the image to download the full syllabus).screen-shot-2016-09-12-at-10-27-07-am","date":"2016-09-12T00:00:00.000Z","formattedDate":"September 12, 2016","tags":[{"label":"opensyllabus","permalink":"/tags/opensyllabus"}],"readingTime":0.185,"hasTruncateMarker":false,"authors":[{"name":"Joe Karaganis","url":"https://github.com/karaganis","imageURL":"https://avatars.githubusercontent.com/u/5721537?v=4","key":"joe"}],"frontMatter":{"slug":"syllabus-of-the-month-september","title":"Syllabus of the Month (September)","authors":["joe"],"tags":["opensyllabus"]},"prevItem":{"title":"Syllabus of the Month (February)","permalink":"/syllabus-of-the-month-february"},"nextItem":{"title":"Why Sociology May Always be the Field of 20 Years Ago","permalink":"/why-sociology-may-always-be-the-field-of-20-years-ago"}},"content":"September\u2019s \u2018Syllabus of the Month\u2019 is a 1994 University of Chicago class, \u201cCurrent Issues in Racism and the Law,\u201d by a junior faculty member you may have heard of.\xa0 (Click the image to download the full syllabus).[![screen-shot-2016-09-12-at-10-27-07-am](./img/note-to-the-readings.webp)](http://www.nytimes.c
1om/packages/pdf/politics/2008OBAMA_LAW/Obama_CoursePk.pdf)"},{"id":"why-sociology-may-always-be-the-field-of-20-years-ago","metadata":{"permalink":"/why-sociology-may-always-be-the-field-of-20-years-ago","source":"@site/blog/2016-05-18-why-sociology-may-always-be-the-field-of-20-years-ago.md","title":"Why Sociology May Always be the Field of 20 Years Ago","description":"Kristin Thomson has posted an excellent piece on Medium that unpacks some of the major demographic features of the top 500 sociology texts in the syllabus corpus\u2013particularly in regard to gender, age, and publication dates.\xa0 To hit a couple of the highlights:","date":"2016-05-18T00:00:00.000Z","formattedDate":"May 18, 2016","tags":[{"label":"opensyllabus","permalink":"/tags/opensyllabus"}],"readingTime":0.785,"hasTruncateMarker":false,"authors":[{"name":"Joe Karaganis","url":"https://github.com/karaganis","imageURL":"https://avatars.githubusercontent.com/u/5721537?v=4","key":"joe"}],"frontMatter":{"slug":"why-sociology-may-always-be-the-field-of-20-years-ago","title":"Why Sociology May Always be the Field of 20 Years Ago","authors":["joe"],"tags":["opensyllabus"]},"prevItem":{"title":"Syllabus of the Month (September)","permalink":"/syllabus-of-the-month-september"},"nextItem":{"title":"The OSP at Two Months","permalink":"/osp-at-two-months"}},"content":"Kristin Thomson has posted [an excellent piece](https://medium.com/@kristinthomson/a-brief-analysis-of-class-assignments-814c6b3bca99#.mv8igzixo) on Medium that unpacks some of the major demographic features of the top 500 sociology texts in the syllabus corpus\u2013particularly in regard to gender, age, and publication dates.\xa0 To hit a couple of the highlights:\\r\\n\\r\\nAmong the works on the list published since 1970, only 24% are by women authors:![Screen Shot 2016-05-18 at 12.07.20 PM](./img/sociology-text-authors.webp)\\r\\n\\r\\nThe periodization of the list also yields interesting results.\xa0 Sociology is predominantly a field built around 20-30 year old texts\u2013a lag that possibly reflects some combination of generational seniority in the field (everyone teaches the major early works by senior figures) and perhaps a relevance horizon beyond which works no longer seem to speak to current issues.\\r\\n\\r\\n![Screen Shot 2016-05-18 at 12.16.04 PM](./img/publication-dates-of-assigned-texts.webp)\\r\\n\\r\\nThis really whets my appetite for the day when we can do this natively in the Syllabus Explorer, across any combination of fields and texts.\xa0 The whole piece is worth a look."},{"id":"osp-at-two-months","metadata":{"permalink":"/osp-at-two-months","source":"@site/blog/2016-03-25-osp-at-two-months.md","title":"The OSP at Two Months","description":"The Open Syllabus Explorer is two months old and just crossed 250,000 visits.\xa0 One of the exciting aspects of the launch for us has been the process of discovering the audience for the project.\xa0 We anticipated strong interest from US academics in the teaching rankings within fields and the \u2018teaching scores\u2019 for their own work.\xa0 The traffic data a
1ppear to bear out that assumption.\xa0 We anticipated interest from the library, publishing, open data, and educational technology worlds, and from students.","date":"2016-03-25T00:00:00.000Z","formattedDate":"March 25, 2016","tags":[{"label":"opensyllabus","permalink":"/tags/opensyllabus"}],"readingTime":4.07,"hasTruncateMarker":false,"authors":[{"name":"Joe Karaganis","url":"https://github.com/karaganis","imageURL":"https://avatars.githubusercontent.com/u/5721537?v=4","key":"joe"}],"frontMatter":{"slug":"osp-at-two-months","title":"The OSP at Two Months","authors":["joe"],"tags":["opensyllabus"]},"prevItem":{"title":"Why Sociology May Always be the Field of 20 Years Ago","permalink":"/why-sociology-may-always-be-the-field-of-20-years-ago"},"nextItem":{"title":"Syllabus of the Month (Jan 2016)","permalink":"/syllabus-of-the-month-jan-2016"}},"content":"![](./img/word-map.webp)\\r\\n\\r\\nThe Open Syllabus Explorer is two months old and just crossed 250,000 visits.\xa0 One of the exciting aspects of the launch for us has been the process of discovering the audience for the project.\xa0 We anticipated strong interest from US academics in the teaching [rankings](http://understandingsociety.blogspot.com/2016/01/graphing-english-speaking-university.html) [within](http://dailynous.com/2016/02/05/philosophy-data-from-the-open-syllabus-project-guest-post-by-andrew-higgins/) [fields](https://earlyamericanists.com/2016/02/08/early-america-in-the-open-syllabus-project/) and the \u2018teaching scores\u2019 for their own work.\xa0 The traffic data a
1ppear to bear out that assumption.\xa0 We anticipated interest from the library, [publishing](http://scholarlykitchen.sspnet.org/2016/01/26/the-open-syllabus-project-altmetrics-and-a-new-dataset/), [open data](http://www.hyperorg.com/blogger/2016/01/22/15345/\\\\), and educational technology worlds, and from [students](http://www.technicianonline.com/opinion/article_fee57920-ce21-11e5-9ded-4f696e0f03b9.html).\\r\\n\\r\\nWe didn\u2019t expect the extent to which the OSP would be treated as an authoritative ranking of Great Books, with a broad audience interested in non or [extra-academic learning](http://lifehacker.com/5188342/top-10-tools-for-a-free-online-education).\xa0 The most powerful hook for media stories (and resulting bumps in traffic) wasn\u2019t the things that interested us most\u2014the significance of introducing a new publishing metric and the potential for exploring the history of fields\u2014but the use of the top-ranked books as a proxy for [being educated](http://qz.com/602956/these-are-the-books-students-at-the-top-us-colleges-are-required-to-read/) or \u2018well-read\u2019, and the enduring connection of these ideas to forms of status anxiety.\xa0 The Washington Post boiled this down to its simplest form: [What Ivy League students are reading that you aren\u2019t](http://What Ivy League students are reading that you aren\u2019t).\\r\\n\\r\\nOther directions have emerged as well. We saw a funny meme take hold that claimed that American students were being indoctrinated in Marxist revolutionary theory, based on the #4 ranking of \u201cThe Communist Manifesto.\u201d\xa0 Bizarrely, this became the most common framework for [international](http://www.welt.de/kultur/literarischewelt/article152177675/Amerikas-Elite-bereitet-mit-Marx-den-Umsturz-vor.html) [stories](http://eot-su.livejournal.com/2329047.html) about the project, alongside a US [right](http://www.breitbart.com/big-government/2016/01/29/karl-marxs-communist-manifesto-ranked-among-top-three-assigned-college-texts/) wing version that juxtaposed high-ranked Marx to the allegedly absent Bible.\xa0 In fact, \u201cThe Communist Manifesto\u201d ranks highly because it is widely taught across a number of fields, particularly sociology, political science, and history, and the Bible doesn\u2019t appear because our methods can\u2019t currently identify texts without authors.\\r\\n\\r\\nWe also accidentally enabled an attack on Time Magazine, which inadvertently placed (male) Evelyn Waugh on a [list of the top 100 women authors](http://time.com/4234719/college-textbooks-female-writers/). This seemed to us to be no big deal, but many others jumped at the opportunity to [shame](http://www.bbc.com/news/entertainment-arts-35667540) [the](http://www.huffingtonpost.co.uk/2016/02/25/time-magazine-evelyn-waugh-best-read-female-authors_n_9317012.html) [magazine](http://nymag.com/thecut/2016/02/10-most-read-male-writers-in-college-classes.html), which in practice just meant shaming the young data researcher who compiled the data and thought he was doing a good thing by highlighting women authors.\xa0 Waugh-related jokes and related scolding actually trended on Twitter for a couple days.\xa0 Our hunch is that a fuller explanation of Time\u2019s \u2018error\u2019 would find that Waugh is one of the losers in literature canon change, and that as a result very few people under 40 have read him or, accordingly, been corrected on his gender during college.\xa0 We may be able to demonstrate this when we fold syllabus dates into the Explorer.\\r\\n\\r\\nWe issued a call for a university-supported [Syllabus Commons](http://chronicle.com/article/H
1ow-a-Syllabus-Commons-/235330) as a means of establishing responsible, open university governance over this valuable resource.\xa0 Many large universities are sitting on tens of thousands of syllabi: Columbia University has around 70,000.\xa0 We\u2019re exploring ways for universities to provide access to these collections on the anonymous, no-republication basis with which we treat other syllabi in our collection.\xa0 The result will hopefully be a \u2018metadata commons,\u2019 in which the basic structured information from the document is available for statistical treatment but the document itself is not.\xa0 Our hope is that this model will avoid some of the battles around strong open access mandates.\xa0 From the university perspective, the best reason to participate may be the value of curricular analysis to re-accreditation or more routine departmental reviews, though we expect a wide range of other beneficiaries, from faculty building courses to university presses trying to understand their markets.\xa0 We have also expanded our faculty donation model and now have around 1000 syllabi that we can make public.\xa0 We will make provisions for these in a later version of the Explorer, and hopefully be able to expand it into a major feature of the tool.\xa0 Somewhat to our surprise, there appears to be a large audience for seeing complete syllabi.\\r\\n\\r\\n![Screen Shot 2016-03-24 at 11.46.02 PM](./img/country-list.webp)Perhaps the most surprising thing has been the uptake of the OSP outside the US, where the OSP\u2019s representation of (primarily) US curricular choices appears to have high value for student, academic, and post-academic learning.\xa0 After the US, the top visiting countries to the Explorer are Ukraine, Russia, and Egypt\u2013clusters of interest for which we have no real explanation.\xa0 After the initial coverage by the [New York Times](http://www.nytimes.com/2016/01/24/opinion/sunday/what-a-million-syllabuses-can-teach-us.html) and Washington Post, roughly two-thirds of visits have come from outside the US.\xa0 We don\u2019t have a good account of how the OSP is being used in these contexts, but that is definitely a subject we\u2019ll try to explore.\\r\\n\\r\\nNext up for the OSP: a point release with coverage of scientific articles, full release of the metadata, permalinks and some other improvements.\\r\\n\\r\\nPs. For another perspective on the geography of use of the OSP, here\u2019s a list of top 20 cities by visits.![Screen Shot 2016-03-25 at 10.10.48 AM](./img/country-list-2.webp)"},{"id":"syllabus-of-the-month-jan-2016","metadata":{"permalink":"/syllabus-of-the-month-jan-2016","source":"@site/blog/2016-01-01-syllabus-of-the-month-jan-2016.md","title":"Syllabus of the Month (Jan 2016)","description":"Keiran Healy is this month\u2019s winner with SOC 710: Social Theory through Complaining.","date":"2016-01-01T00:00:00.000Z","formattedDate":"January 1, 2016","tags":[{"label":"opensyllabus","permalink":"/tags/opensyllabus"}],"readingTime":0.07,"hasTruncateMarker":false,"authors":[{"name":"Joe Karaganis","url":"https://github.com/karaganis","imageURL":"https://avatars.githubusercontent.com/u/5721537?v=4","key":"joe"}],"frontMatter":{"slug":"syllabus-of-the-month-jan-2016","title":"Syllabus of the Month (Jan 2016)","authors":["joe"],"tags":["opensyllabus"]},"prevItem":{"title":"The OSP at Two Months","permalink":"/osp-at-two-months"},"nextItem":{"title":"Open Syllabus Talk at IODC 2015","permalink":"/open-syllabus-talk-at-iodc-2015"}},"content":"Keiran Healy is this month\u2019s winner with SOC 710: Social Theory through Complaining.\\r\\n\\r\\n![Healy](./img/healy.webp)"},{"id":"open-syllabus-talk-at-iodc-2015","metadata":{"permalink":"/open-syllabus-talk-at-iodc-2015","source":"@site/blog/2015-08-07-open-syllabus-talk-at-iodc-2015.md","title":"Open Syllabus Talk at IODC 2015","description":"Here\u2019s an overview of OSP progress in the last year, with previews of some of the candy we\u2019ll be making public in the next months (from the International Open Data Conference 2015).","date":"2015-08-07T00:00:00.000Z","formattedDate":"August 7, 2015","tags":[{"label":"opensyllabus","permalink":"/tags/opensyllabus"}],"readingTime":0.19,"hasTruncateMarker":false,"authors":[{"name":"Joe Karaganis","url":"https://github.com/karaganis","imageURL":"https://avatars.githubusercontent.com/u/5721537?v=4","key":"joe"}],"frontMatter":{"slug":"open-syllabus-talk-at-iodc-2015","title":"Open Syllabus Talk at IODC 2015","authors":["joe"],"tags":["opensyllabus"]},"prevItem":{"title":"Syllabus of the Month (Jan 2016)","permalink":"/syllabus-of-the-month-jan-2016"}},"content":"Here\u2019s an overview of OSP progress in the last year, with previews of some of the candy we\u2019ll be making public in the next months (from the International Open Data Conference 2015).\\r\\n\\r\\n<iframe width=\\"560\\" height=\\"315\\" src=\\"https://www.youtube.com/embed/NUtQBC4SqTU?start=844\\" frameborder=\\"0\\" allowFullScreen=\\"\\"></iframe>"}]}')}}]);

Line numbers count LF bytes from the start of the resource, as the search results do. Vendor segments are library code the classifier recognised; they are stored but not indexed. Bytes are shown as Latin1 characters, one per byte.