Social Science in the Law: Applications of Social Science in the Law: Regulating (Hetero) Sexual Offences in British Columbia, 1885-1940
Bibliographic record
Abstract
... Feminists have examined these issues in relation to the adjudication of criminal cases involving women accused (bat-tered woman syndrome - BWS; pre-menstrual syndrome - PMS) and women complainants/victims (rape trauma syn-drome - RTS) as well as cases in other areas of law such as sex discrimination and sexual harassment. ... However, the almost routine introduction of medical testimony at hearings involving white people also suggests that the scientific determination of sexual purity or impurity was most relevant in such cases; perhaps because generally speaking neither accused nor complainant/victim was automatically assumed to be unfit. ... In their attempts to neutralize or weaken medical testimony by discrediting the complainant/victim in a case, defense counsel were able to trade on pervasive assumptions about sex and sexuality and were allowed incredibly wide latitude in questioning complainants/victims (and other prosecution witnesses), particularly at the preliminary hearing stage. ... However, in a 1923 case in which an Aboriginal accused was charged with the rape of a married, white women, strong medical testimony arguably contrib-uted to both conviction and sentence since married women were otherwise so unsuccessful in sexual offence cases. ... In considering the impact of medical evidence, it also is important to bear in mind that expert testimony introduced at a preliminary hearing was often repeated verbatim at trial where the judge's direction to a jury could be critical to the out-come of a case. ...
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.019 | 0.015 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".