The Textbook Affordability Puzzle: Perspectives from Three of the Pieces
Bibliographic record
Abstract
Many institutions focus textbook affordability efforts through open educational resources, but that isn’t the only option available to provide students with affordable course materials. This paper outlines how the University of Central Florida Libraries successfully leveraged its e-book collections to support textbook affordability efforts. A description of the initiative is provided from three perspectives; an associate director, the textbook affordability librarian, and an acquisitions librarian. Included will be the genesis of the program, methodology used, and how data collected from the initiative were used to gain a new position at the university, a Textbook Affordability Librarian. As part of this initiative, various avenues were developed for faculty outreach and collaboration. Instead of a solo effort, there was a strong emphasis on collaboration with internal and external library partners. The final part of the paper is a discussion of considerations for purchasing materials for use as a course text.
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 | 0.019 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.018 | 0.051 |
| Scholarly communication | 0.030 | 0.042 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.013 | 0.025 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".