What Can 100,000 Books Tell Us about the International Public Library e-lending Landscape?
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 investigated the relative availability of e-books to libraries for e-lending in five English-language countries, and analysed their licence terms and prices. Method: We created a unique dataset recording author, publisher, price and terms for 100,000 titles and 388,045 e-lending licences across Australia, New Zealand, Canada, the United States and United Kingdom via aggregator Overdrive. We developed new algorithms to estimate the original publication year for each title, and to match titles across jurisdictions.Analysis: We examined the relationships between title price, age, terms, jurisdiction, publisher and publisher type using various statistical analyses and machine learning. Results: Price and licence differences across countries are largely attributable to ‘Big 5’ publishers. Prices are largely independent of title age (unless the title is in the public domain) or the rights libraries obtain in exchange. Licence terms are not affected by age either, meaning that the most restrictive terms are often applied to older, less demanded books. Conclusions: By setting terms independent of titles’ value to libraries, publishers may discourage libraries from adding older and less-demanded books to their collections. We will test this hypothesis in a follow-up library survey.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it