The Natural Right To Parody: Assessing The (Potential) Parody/Satire Dichotomies In American And Canadian Copyright Laws
Bibliographic record
Abstract
This paper argues that the right to expressing oneself through parodies should constitute part of the core freedom of expression of a normative copyright regime. By drawing upon natural law legal theories, the paper proposes a legal definition of parody that would help to bring the copyright jurisprudence of a jurisdiction more in line with its free speech tradition. It argues that a broad parody definition, one that encompasses a great variety of expressive works but would not compete with the original and its derivatives in the market, is preferable to a narrow one. The paper then explains why the parody defence in American law and the parody exception in the Canadian copyright statute should follow the proposed parody definition, which would properly balance the rights of copyright owners with those of users.
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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 | 0.016 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.021 | 0.052 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".