Rethinking the “Rough Sex Defence” in Canada: Replies to Sheehy et al.
Bibliographic record
Abstract
This article critiques the arguments advanced by Elizabeth Sheehy, Isabel Grant, and Lise Gotell in their 2023 Alberta Law Review article, “Resurrecting ‘She Asked for It’: The Rough Sex defence in Canadian Courts.” Sheehy et al. trace a rise in both “rough sex” and “sex games gone wrong” defences in cases involving bodily harm and death in Canada and the United Kingdom, equating these defences to an updated version of the “she asked for it” defence. They argue that consent should not be a valid defence for bodily harm resulting from sexual activity unless such harm was unforeseeable, emphasizing that those engaging in violent sex acts should bear the risk of serious injury or death to their partners. Concurring with Sheehy et al. on the gravity of gender-based violence, this article problematizes their broad conflation of Bondage-Discipline, Dominance-Submission, and Sadism-Masochism or Sadomasochism (BDSM), rough sex, and sexual assault. Drawing primarily on queer theory, anti-carceral feminism, and the insights of BDSM subcultures, the authors argue — separately and among other points — that Sheehy et al.’s framing of “rough sex” perpetuates a carceral, paranoid, and partisan approach to sexual justice and stigmatizes BDSM practitioners, scapegoating them for failures in sexual assault prosecutions. Interrogating the limits of their position, this article advocates a more complex understanding of sexual consent, accountability, and harm.
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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 | 0.031 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.051 | 0.043 |
| Scholarly communication | 0.020 | 0.012 |
| Open science | 0.011 | 0.008 |
| Research integrity | 0.058 | 0.059 |
| Insufficient payload (model declined to judge) | 0.005 | 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".