Available - But not Accessible? Investigating Publisher e-lending Licensing Practices
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.
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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 | 0.044 | 0.134 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".