Exploring Gender-Based Violence in Canadian Prisons: A Scoping Review
Bibliographic record
Abstract
Background In Canada, prison policies, programs, and practices have been shaped by the historical influences of sex-segregated prisons (i.e., not gender segregated; see Ricciardelli and Bartlett, 2022). Nevertheless, sex segregation has not “stopped” gender-based violence from occurring in prisons. There continues to be risk and security concerns for gender-based violence, including but not limited to 2SLGBTQIA+ people who experience such violence from other prisoners and other correctional staff. As such, our purpose was to review the literature on gender-based violence in Canadian prisons, with an eye to policy, to offer suggested considerations for pathways forward. Objectives We sought to map the existing literature on gender-based violence in Canadian prisons. Our guiding research question is: What is the existing literature on prison policies, programs, and practices that have contributed (and continue to contribute) to the historical and contemporary exclusion, marginalization, and gender-based violence of people of diverse gender identifications? Results Forty-seven articles (n = 47) met the criteria for inclusion in the review. Most articles included in the review originate from the USA (n=34). The remaining articles originate from Canada (n=6), Australia (n=3), the United Kingdom (n=3), and Israel (n=1). Key messages Violence against female and gender diverse persons in prison contexts remain an ongoing issue. Historically, prison policies denied transgender (herein ‘trans’) people who are incarcerated access to proper healthcare and overlooked gender identity. Today, gender identity and lived experience are important factors to consider in the placement of trans people who are incarcerated. Rehabilitative programming and services for female and gender diverse people in prison contexts should remain accessible. Methodology The Arksey and O’Malley’s (2005) methodological framework for scoping reviews is used to identify, describe, and summarize the literature. Several databases were searched for relevant literature including Policy Commons Open Access; Canadian Electronic Library from Canada Commons; HeinOnline Government, Politics & Law in Canada; PsycINFO; Criminal Justice Abstracts; Sociological Abstracts, JSTOR, Web of Science, ProQuest Dissertations and Theses Global, and the Government of Canada website. We used the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist (Tricco et al., 2018) for complete and transparent reporting on the conduct of the literature review.
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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 | 0.011 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.026 | 0.032 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".