Laying the Foundation for Copyright Policy and Practice in Canadian Universities
Bibliographic record
Abstract
Due to significant changes in the Canadian copyright system, universities are seeking new ways to address the use of copyrighted works within their institutions. While the law provides quite a bit of leeway for use of copyrighted materials for educational and research purposes, the response by Canadian universities and related associations has not been to fully embrace their legal rights – rather, they have taken an approach that places emphasis on risk avoidance rather than maximizing use of materials, unlike their American counterparts. In the U.S., where educational fair use is arguably less flexible in application than fair dealing, there is a higher level of copyright advocacy among professional associations, and several sets of best practices have been created to guide the application of copyright to educational use of materials.\nCanada is lagging behind the U.S. in this respect, placing Canadian universities at a relative disadvantage. The goal of this study is to lay the foundation for the development of policies and guidelines in the use of copyrighted works, and the provision of copyright literacy education in universities. The research will be undertaken from a critical perspective, with the goal of promoting fair dealing and other exceptions as user rights within the institution, and a reduction in risk aversion.\nThe methodology employed is both qualitative and quantitative and includes legal analysis, content analysis of policies and guidelines, and collection of survey data.
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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 | 0.051 | 0.099 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.055 | 0.034 |
| Scholarly communication | 0.029 | 0.010 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".