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Record W4401228156 · doi:10.1177/08874034241268986

Correctional Transgender Policy in Canada’s Federal Prison System

2024· article· en· W4401228156 on OpenAlexafffundabout
Gillian Foley, Marcella Siqueira Cassiano, Rosemary Ricciardelli, James Gacek

Bibliographic record

VenueCriminal Justice Policy Review · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsUniversity of ReginaUniversity of WinnipegMemorial University of Newfoundland
FundersCanadian Institutes of Health Research
KeywordsPrisonTransgenderCompromiseDiscretionOperationalizationUnintended consequencesCriminologyPolitical scienceWork (physics)Sex offenderPsychologyLawEngineering

Abstract

fetched live from OpenAlex

Since December 2017, Canada's federal correctional system provides prisoners the opportunity to be assigned to living units according to their self-identified gender. Still organized around sex, conceptually and spatially, prison policies and procedures surrounding transgender prisoners require navigation to adhere to the rights of all prisoners. Based on interviews conducted between October 2019 and October 2021 with 74 correctional officers (COs) from the Canadian federal prison system, we discuss how correctional officers view and operationalize Canada's transgender policy to understand its unintended consequences for both prisoners and prison staff. Unintended consequences revolve around the potential risk for prisoner victimization, prisoner pregnancy, lack of adequate housing, strip search complications, officers' fear of being labeled transphobic, and uncertainty and discretion; all having effects on staff wellness. The policy, although well-intended, may potentially compromise prisoner safety, making correctional work even more stressful.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.784
Threshold uncertainty score0.910

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.005
Science and technology studies0.0110.002
Scholarly communication0.0040.001
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.053
GPT teacher head0.373
Teacher spread0.321 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2024
Admission routes3
Has abstractyes

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