Bibliographic record
Abstract
Sexual assault is an area of law that has been fraught with misogyny and racism. This paper attempts to contribute to the literature on gender-justice in the sexual assault context by relying on an intersectional analysis that examines religion and culture. In doing so, I discuss the needs of a small minority of women. Though their numbers may be few in Canada, adequately responding to the plight of niqab-wearing women in this context is both just and will serve to ameliorate the workings of the judicial system for all women. In Toronto, Ontario, a Muslim woman complainant recently made a request to wear her niqab while giving testimony in a preliminary inquiry in which she alleged that two accuseds sexually assaulted her over a period of several years. The accuseds’ lawyers objected to the complainant wearing her niqab arguing that it prevented them from effectively cross-examining her. This paper will argue that the prosecution and adjudication of the offence of sexual assault must be more inclusive of the needs of Muslim women who cover their faces. My interest with this work is in ensuring that women’s equality is furthered, that women from minority groups in particular are not in the unhelpful position of having to choose between their cultural or religious beliefs and other fundamental rights that they are entitled to.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.030 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".