Sexual Violence Procedures in Canadian Correctional Facilities
Bibliographic record
Abstract
Sexual violence is an unspoken aspect of life for inmates in Canadian prisons. Statistics Canada figures suggest that roughly 1 sexual assault occurs for every 1100 federally incarcerated persons, but the Office of the Correctional Investigator’s (2020) report maintains that official statistics underestimate the extent of sexual violence in prisons. Presently, only the federal incarceration system and 1 out of 13 provincial / territorial systems have publicly available sexual violence policies. More accurate data and victim sensitive treatment would be fostered by a nationally standardized sexual violence procedure. This procedure should be informed by a responsive approach to treatment that prioritizes the needs of survivors over those of the criminal justice system. To help produce this resource, I conducted a systematic literature review of: (1) sexual assault survivors’ needs, and (2) sexual assault in Canadian correctional facilities, with an emphasis on content related to prevalence, prevention, disclosure, victim treatment, and safety protocols. Based on themes from these bodies of literature (e.g., victims’ needs for medical and psychological care, establishing mandatory staff reporting), I developed a codebook to guide a review of existing sexual assault procedures in Canadian correctional facilities. In combination, the literature review and content analysis provide the foundations for a nationally standardized procedure.
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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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.026 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".