Review of Canadian University Fair Dealing Policies
Bibliographic record
Abstract
The past three years have seen a number of changes in the area of copyright law, particularly in the area of education. As a result, Canadian universities have had to make policy decisions to account for these changes and the resulting expansion of fair dealing rights. The content and consistency of the resulting policies may have a significant effect on the future interpretation of fair dealing rights. In this paper I analyze the current state of fair dealing policies and supporting information found on university web sites. I conclude that an ideal fair dealing policy is open ended and flexible, and incorporates mention of the significant elements of copyright legislation, court decisions, and other areas of law, in a way that is accessible to its intended audience of faculty and instructors.
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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 | 0.033 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.033 | 0.039 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".