Current Complications in the Law on Myths and Stereotypes
Bibliographic record
Abstract
Myths and stereotypes represent an ongoing problem in Canadian sexual assault trials. Often, and paradigmatically, defence lawyers and trial judges rely on discredited sexist assumptions to the prejudice of female sexual assault complainants. However, a review of the recent appellate case law reveals many cases that do not fit this paradigm. Complications that have arisen include stereotypes about men or accused persons, legitimate defence arguments misidentified as stereotypes, close cases where reasonable people disagree about whether stereotypes have been invoked, and prejudicial forms of reasoning based other axes of discrimination. This paper surveys these developments and assesses an attempt by the Court of Appeal for Ontario to bring order to this area of law in the 2021 case of R v JC.
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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 | 0.069 | 0.111 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.027 | 0.090 |
| Scholarly communication | 0.025 | 0.017 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.015 | 0.025 |
| 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".