The Use of Arguments about Myths and Stereotypes to Appeal Sexual Assault Convictions in Canada
Bibliographic record
Abstract
Canadian defence counsel have recently begun appealing sexual assault convictions by arguing that a trial judge applied myths and stereotypes (M&S) against the accused. This phenomenon is surprising because this country’s focus on M&S in sexual assault law has almost exclusively concerned improper assumptions that operate against the complainant and the Crown and risk producing perverse acquittals. This thesis reviews this new defence strategy with reference to three decades of appellate case law and scholarship. It advances definitions of M&S as well as principles for understanding the evidentiary effects of their recognition as such, and it categorizes various defence attempts to invoke M&S in conviction appeals, concluding that some have more merit than others. Emerging from this analysis is a more consistent, coherent role for the M&S doctrine in sexual assault law – one which should assist the Canadian bench, bar and academy in distinguishing legitimate M&S arguments from strained ones.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| 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".