Building Open Education Capacity: Introducing the Canadian Code of Best Practices in Fair Dealing for Open Educational Resources
Bibliographic record
Abstract
This article builds upon a presentation given at the 2024 ABC Copyright Conference in which the authors outlined the process for adapting the Code of Best Practices in Fair Use for Open Educational Resources (OER) for a Canadian audience. Originally published in 2021, the U.S. Code is an important tool for evaluating common OER use cases, providing a framework of analysis that can guide a creator towards making judiciously defensible fair use decisions. Alongside practical guidance, the Code represents a significant contribution in support of the United Nations Educational, Scientific and Cultural Organization (UNESCO)’s Recommendation on OER, which encourages member states to build capacity concerning exceptions and limitations for the use of copyrighted works for educational and research purposes. Supported by the Canadian Association of Research Libraries, the Canadian Adaptation Working Group began their adaptation process in late 2021 and the final Code was published in early 2024. In addition to providing an overview of the adaptation process, this article offers a comprehensive summary of the legal considerations that informed the writing of the Code and provides examples of how the Code has been operationalized at educational institutions in Canada.
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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 | 0.089 | 0.146 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.035 | 0.067 |
| Scholarly communication | 0.034 | 0.015 |
| Open science | 0.008 | 0.019 |
| Research integrity | 0.019 | 0.022 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".