“It’s Sexual Assault. It’s Barbaric”: Strip Searching in Women’s Prisons as State-Inflicted Sexual Assault
Bibliographic record
Abstract
Rationale: In the era of the #MeToo movement, sexual assault has emerged from the shadows to become a dominant topic of public and scholarly conversation. Yet women in prison have largely been left out of these conversations, particularly as it relates to their experiences of being strip searched. Method: Five cisgender women were interviewed about their experiences being strip searched while imprisoned in Canada. Findings: Findings demonstrate that strip searching is a form of sexual assault. Women were unable to say “no” to being strip searched due to power imbalances and fear of serious consequences. Experiences of prior sexual victimization made being strip searched particularly harmful. Discussion: This study shows that structural violence occurring behind prison walls is a replication of structural violence occurring in the community. That strip-searching policies and practices are developed and implemented by the state necessarily means it is state-inflicted sexual assault. I theorize that strip searching is not understood as sexual assault because imprisoned women are relegated to a class of subhumanness for which humane treatment is not required. Implications: Implications for reducing the harms of strip searching are discussed, aimed at moving toward the abolishment of strip searching as a practice in women’s prisons.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.012 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".