Code of Best Practices in Fair Use for Open Educational Resources: A Guide for Authors, Adapters & Adopters of\nOpenly Licensed Teaching and Learning Materials
Bibliographic record
Abstract
Code of Best Practices in Fair Use for Open Educational Resources 5\nOpen Educational Resources and Fair Use ... 5\nOpen Educational Resources, Inserts, and Universal Access .... 6\nBehind the Code: Copyright Flexibilities and Fair Use in OER 7\nApplying This Code 9\nTHE CODE: Principles, Considerations, and Hard Cases ... 11\nA. Using inserts as objects of criticism and commentary 11\nB. Including inserts for the purpose of illustration 12\nC. Incorporating content as learning resource materials 13\nD. Repurposing pedagogical content from existing educational materials ... 14\nSignaling Fair Use . 16\nAppendix One: Background findings .... 18\nAppendix Two: Fair Use Then and Now ... 24\nThe Fair Use Right and the Function of Copyright ... 24\nFair Use and Education ... 25\nFair Use and Access to Educational Materials .... 26\nThe Developing Law of Fair Use 26\nFair Use in a Nutshell ... 28\nAppendix Three: Educational Fair Dealing in Canada . 30\nThe Development of Fair Dealing in Canada . 30\nThe Common History of Fair Dealing and Fair Use .. 30\nFair Dealing as a User Right 31\nThe Copyright Pentalogy 32\nThe 2012 Copyright Modernization Act .... 33\nUS-Canada Cross-Border Considerations for the OER Community 34\nAppendix Four: Educational Exceptions and Quotations Rights Around the World .. 37\nAppendix Five: Beyond fair use: more good news about copyright for OER .... 40\nThe Public Domain: Materials and Content not Protected by Copyright .. 40\nBuilt-in Constraints on the Scope of Copyright 42\nWhat About Other Areas of Intellectual Property? Trademark Law Considered 44\nWhat About Patent Law? ... 45
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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 | 0.037 | 0.123 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.068 | 0.088 |
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".