Gendered and Racialized Violence, Strip Searches, Sexual Assault and Abuse of Prosecutorial Power
Bibliographic record
Abstract
In the early morning hours of September 6, 2008, S.B., a twenty-seven-year old African Canadian, experienced the depths of depravity at the hands of five officers with the Ottawa Police Service. She was arrested unlawfully for effectively questioning why she had been stopped by the police, taken to the police station where she was assaulted and strip searched in the presence of a number of male officers, one of whom cut off her shirt and bra with a pair of scissors, and then left half-naked in a cell for over three hours. When she left the police station, she found herself charged with assaulting a police officer. The case was reviewed on a number of occasions by senior prosecutors who believed that the prosecution of S.B. was in the public interest. Two years after the incident, a trial judge stayed the charge concluding that it was a travesty and that what happened to her was an indignity to a human being. This article examines a number of dimensions of the case including (i) the elements of gendered and racialized stereotyping and violence in the case; (ii) the degree of departure from R. v. Golden, the leading Canadian case on strip searches; (iii) whether what occured was a sexual assault; and finally, (iv) the issue of accountability and the decision of the Ottawa Crown Attorney's Office to pursue a prosecution against S.B. as opposed to the police officers involved.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.028 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".