Bibliographic record
Abstract
This Chapter recounts the history of fair use and fair dealing. It traces the shared common law origins of fair use and fair dealing in English and American copyright law, and shows that the enactment of the 1911 UK Copyright Act - the basis for current copyright laws of most Commonwealth jurisdictions - was not designed to cause any major alteration in the common law of fair dealing. The historical record shows that the distinction between US-style open-ended fair use and fair dealing as a myth: the codification of fair dealing in 1911 was not designed to limit its application to the enumerated purposes included in the statute.The question of whether the list of enumerated purposes is exhaustive or, instead, illustrative of a broader principle has never been put squarely before the courts, let alone the higher courts, and certainly not in Canada. Therefore, the question of whether fair dealing in Canada can apply to purposes that are not explicitly mentioned in the Copyright Act is an open one, and as this Chapter shows, can and should be answered affirmatively. Doing so will not transplant a foreign legal concept. Rather, it will reunite present copyright doctrine with its rich and historic roots that were latent but never discarded. Embracing an open-ended fair dealing is the only logical application of the Supreme Court of Canada latest decisions and Parliament’s action, and the only interpretation of the Act that can be internally, historically, and constitutionally coherent.
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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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.028 | 0.050 |
| Scholarly communication | 0.024 | 0.010 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.007 | 0.014 |
| 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".