Controlled Digital Lending of Library Books in Canada
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
This paper explores legal considerations for how libraries in Canada can lend digital copies of books. It is an adaptation of A Whitepaper on Controlled Digital Lending of Library Books by David R. Hansen and Kyle K. Courtney, and draws heavily on this source in its content, with the permission of the authors. Our paper considers the legal and policy rationales for the process—“controlled digital lending”—in Canada, as well as a variety of risk factors and practical considerations that can guide libraries seeking to implement such lending, with the intention of helping Canadian libraries to explore controlled digital lending in our own Canadian legal and policy context. Our goal is to help libraries and their lawyers become better informed about controlled digital lending as an approach, offer the basis of the legal rationale for its use in Canada, and suggest situations in which this rationale might be strongest.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.038 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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