Available - But not Accessible? Investigating Publisher e-lending Licensing Practices
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Introduction: We report our mixed-methods investigation of publishers’ licensing practices, which affect the books public libraries can offer for e-lending.Method: We created unique datasets recording pricing, availability and licence terms for sampled titles offered by e-book aggregators to public libraries across Australia, New Zealand, Canada, the United States and United Kingdom. A third dataset records dates of availability for recent bestsellers. We conducted follow-up interviews with representatives of 5 e-book aggregators.Analysis: We quantitatively analysed availability, licence terms and price across all aggregators in Australia, snapshotting the competitive playing field in a single jurisdiction. We also compared availability and terms for the same titles from one aggregator across five jurisdictions, and measured how long it took for a sample of recent bestsellers to become available for e-lending. We used data from the aggregator interviews to explain the quantitative findings.Results: Contrary to aggregator expectations, we found considerable intra-jurisdictional price and licence differences. We also found numerous differences across jurisdictions.Conclusions: While availability was better than anticipated, licensing practices make it infeasible for libraries to purchase certain kinds of e-book (particularly older titles). Confidentiality requirements make it difficult for libraries to shop (and aggregators to compete) on price and terms.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.005 | 0.020 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 it