Sauce for the Gander: The False Symmetry of Myths and Stereotypes Against the Accused in Sexual Assault Trials
Bibliographic record
Abstract
As sexual assault laws evolved to be expressed in sex-neutral language, so too have arguments relating to myths and stereotypes in the context of sexual offences. This article evaluates the argument that myths and stereotypes, traditionally a reflection of systemic discrimination against female complainants in rape trials, also operate against male accused. Understood correctly, this trend repackages anti-feminist backlash to early equality reform efforts, ultimately working to revive the belief that women’s accusations of sexual assault are often false. The Supreme Court of Canada’s decision in R v Kruk affirms that this historical context matters when it comes to claims of stereotyping in sexual assault cases. While there is reason to be alive to the issue of stereotyping the accused—as in cases of racism or homophobia—those seeking to bring evidence of myths and stereotypes should not rely on the formal equality argument that what applies to the “goose” applies equally to the “gander.” Pendant que les lois sur les agressions sexuelles ont évolué pour être formulées dans un langage neutre sur le plan du genre, les arguments liés aux mythes et aux stéréotypes dans le contexte des infractions sexuelles ont également évolué. Le présent article évalue l’argument selon lequel les mythes et les stéréotypes, qui reflètent traditionnellement la discrimination systémique à l’égard des plaignantes dans les procès pour viol, jouent également en défaveur des accusés masculins. Bien comprise, cette tendance reformule la réaction antiféministe face aux premiers efforts de réforme en faveur de l’égalité, contribuant finalement à raviver la croyance selon laquelle les accusations d’agression sexuelle portées par les femmes sont souvent fausses. L’arrêt R. c. Kruk de la Cour suprême du Canada confirme que ce contexte historique est important lorsque l’on invoque des stéréotypes dans des affaires d’agression sexuelle. Bien qu’il y ait lieu d’être conscient du problème des stéréotypes à l’égard des accusés, comme dans les cas de racisme ou d’homophobie, les personnes qui cherchent à apporter des preuves de mythes et de stéréotypes ne devraient pas s’appuyer sur l’argument officiel de l’égalité selon lequel ce qui s’applique à l’« oie » s’applique également au « jars »
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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 | 0.026 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.068 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.015 |
| Insufficient payload (model declined to judge) | 0.003 | 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".