The Rise and Fall of Duress (or How Duress Changed Necessity Before Being Excluded by Self-Defence)
Bibliographic record
Abstract
The Supreme Court of Canada decision in R. v. Ryan significantly reshaped both the common law and statutory defenses of duress, harmonizing them and, in the case of the common law defense, fully articulating it for the first time. The decision is admirable for that reason. This paper argues that two further results can also be seen.\nFirst, the defense of necessity is a common law one which is conceptually similar to duress. The Court's reasoning at a policy level about duress ought therefore to be applicable to necessity: this paper traces the ways in which that latter defense ought therefore to be re-articulated.\nSecond, a major benefit of Ryan was that it removed the need to apply one defense to the person who actually committed an offense while using a different one for a party to an offense. Ironically, only weeks after Ryan was decided, The Citizen’s Arrest and Self-defense Act came into force. It is argued that the newly created version of self-defense in fact overlaps entirely with the circumstances in which the common law defense of duress would have applied. The net result is that this Parliamentary action restores the need to use different defenses for the principal and parties, thus undoing a good deal of the benefit of Ryan.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.027 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".