Violent Exposures, Exposing Violence: Gender, Anti-Blackness and the Strip-Searching of Black Women and Girls in Canada
Bibliographic record
Abstract
In recent years, more media attention has been given to the routinisation of police strip-searches in Canada. As with many violent policing practices, the routine use of strip-searching disproportionately affects Black, Indigenous, and racialised women. This article investigates the legal archives of two cases of the strip-searching of Black women and girls in Canada – the case of S.B. who was violently strip-searched by four Ottawa police officers in 2008 and the case of three 12-year-old girls who were strip-searched in a Halifax public school in 1995. This article demonstrates that the exposure of Black women’s and girls’ bodies that occurs in the strip-search encounter is part of the matrix of gendered anti-Blackness. In tracing the moves that the state makes to erase the sexualised violence of the strip-search, this paper suggests that the strip search be understood as a form of gendered anti-Black terror – a technology of violence that functions to evict Black women and girls from personhood. The disciplinary technology of the strip-search is one way in which the state exercises its sovereign power and marks Black women’s and girls’ bodies as violable bodies. I argue that the weaponisation of bodily exposure has a long legacy, and as a highly visual and spectacular encounter, the strip-search cases point to a particular kind of persistent corporeal violence.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.050 | 0.020 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".