On the Uneasy Interface between Economic Rights, Moral Rights and Users’ Rights in Copyright Law: Can Canada Learn from the UK Experience?
Bibliographic record
Abstract
Copyright in Canada is subject to a number of statutory defences, of which parodies and non-commercial user-generated content (UGC) are but two examples. However, the interface between these defences and the protection of moral rights is not very clearly delineated in Canada’s Copyright Act. The statutory defences appear to immunise a user from liability for traditional copyright infringement but not from claims of moral rights infringement. Under this fragmentary approach, users engaging in acts of fair dealing or in the production of non-commercial UGC might still find themselves vulnerable to attack from author-claimants alleging that their moral rights have been violated. Through a comparative survey of key legislative provisions in Canada and the United Kingdom, this article explores the extent to which Canada can learn from the UK experience, and considers the viability of streamlining the scope of the statutory defences to copyright infringement, in order to clarify the interface between users’ rights, moral rights and economic rights.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.038 | 0.041 |
| Scholarly communication | 0.019 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 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".