Textbooks Are Expensive, But OER Can Be Challenging: Providing E-Textbook Access Through the Library
Bibliographic record
Abstract
Research has shown that textbook costs are rising. Open educational resources (OER), though increasingly popular, are not available for all courses and can be difficult to adopt, particularly for contingent faculty. In response to the textbook crisis and the limitations of OER, Temple University has sought alternative ways to provide textbook access to students. We have promoted OER through a grant program since 2011 and offer a website to expose assigned readings that the Libraries own in e-book format. In 2018, the Libraries also began purchasing e-textbooks. The campus bookstore sends a list of assigned books each semester. We review the list according to criteria such as e-availability, existing library holdings, and previous assignment of the same materials. In spring 2018, Temple University Libraries purchased 38 assigned texts as e-books and added these to the e-textbook website. These e-books had heavier usage than other e-books purchased during the same period, and the Libraries plan to continue this practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.174 | 0.065 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".