From Fair Dealing to Fair Use: How Universities Have Adapted to the Changing Copyright Landscape in Canada
Bibliographic record
Abstract
[From introduction]: “The first half of this chapter provides a synopsis of the major legislative, jurisprudential, and policy changes that have had an impact on higher education in Canada over the past ten years, with a focus on how these changes have transformed the way that copyright is managed in higher education. The second half focuses on the role that libraries have played in this management, as Canadian universities and colleges have frequently turned to their libraries for help with navigating—and managing—this new copyright landscape. Finally, the chapter concludes with a few thoughts about the future of copyright management in higher education in Canada as institutions determine paths forward in the aftermath of the Access Copyright v. York case.”
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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 | 0.012 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.056 | 0.035 |
| Scholarly communication | 0.035 | 0.009 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".