Locking Out Lawful Users: Fair Dealing and Anti-Circumvention in Bill C-32
Bibliographic record
Abstract
This chapter examines the potential impact of the proposed fair dealing and anti-circumvention provisions in Canada’s most recent copyright reform bill, Bill C-32. I suggest that the minimal expansion of the fair dealing defence to cover “new” purposes, as well as the addition of a few new user exceptions, while welcome, is insufficient to ensure the breadth of user defences that the copyright balance demands. Moreover, the extensive protection of technological protection measures without any regard for lawful uses of copyright material has the potential to effectively eviscerate fair dealing in the digital age. Many acts permitted in relation to owned content can be prevented by the use of TPMs, and would be rendered unlawful by the proposed anti-circumvention provisions. To extend legal protection to TPMs in a manner that fails to guard the contours of fair dealing and user rights from technological encroachment is to undermine the social goals of the copyright system, and to relinquish the policy balancing act performed in their name.
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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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.018 |
| Scholarly communication | 0.017 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.018 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 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".