Resurrecting “She Asked for It”: The Rough Sex Defence in Canada
Bibliographic record
Abstract
Internationally, the “rough sex defence” appears to be on the rise. Used to suggest that women enjoy violence as part of “sex play,” it invites judges and jurors to find either consent to acts causing bodily harm or an honest but mistaken belief in consent. Our review of the Canadian case law from 1988–2021 examines how courts approach this defence. We found that the defence is gendered, with only men as perpetrators and overwhelmingly women on the receiving end. We explore themes from the cases including the role of pornography, the trivialization of bodily harm, the mischaracterization of strangulation, and how consent to some sexual activity undermines women’s credibility. We conclude that consent should be barred as a defence to causing bodily harm unless that harm was unforeseeable when inflicted.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.030 | 0.015 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".