An Experiment in Open-Access Textbook Publishing: Changing the World One Textbook at a Time
Bibliographic record
Abstract
The revolt against the ever-increasing costs of postsecondary texts has begun. No one can deny that reselling texts, sharing texts, e-book reserves, and free copies that are resold have forced the commercial publishers to take action. But at what cost to higher education? Just as the cable monopolies are beginning to lose ground to other delivery systems of broadcast content, so too are textbook companies losing ground to other forms of delivery. Most commercially developed e-textbooks are little more than enhanced print editions and have limited access and restrictions on printing and downloading the content. Open-access texts solve many of these problems, but, as many now realize, ‘open’ does not equal ‘no cost.’ This article will explore some of the forces that are driving the open-access phenomenon, and describes the joint effort by the University Press of Florida and the University of Florida Department of Mathematics project for open-access calculus texts.
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 | 0.029 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.013 | 0.026 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.024 | 0.004 |
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".