Transforming Choices: The Marginalization of Gender-Specific Policy Making in Canadian Approaches to Women's Federal Imprisonment
Bibliographic record
Abstract
In 2008, the Correctional Service of Canada accepted the 2007 report of Correctional Service of Canada Review Panel, A Roadmap to Strengthening Public Safety. This document provides the basis for the “transformation agenda” that now guides the administration of imprisonment for all populations. For federally sentenced women, this approach coexists with the recommendations made by the Task Force for Federally Sentenced Women in their 1990 report, Creating Choices. This analysis compares the composition of the committees responsible for these documents, their mandates, their consultation processes, and how these factors shaped their respective conclusions and recommendations. While Creating Choices can be understood as a feminist policy document, Roadmap employs a gender-neutral law-and-order discourse—its authors assume that the recommendations they make for male prisoners can be applied to women and minority groups. Shifting discourses about women, gender equality, and imprisonment are evident and are consistent with Laureen Snider's argument that the dominant conceptualization of imprisoned women has changed from that of the “woman in trouble” to that of the “atavistic woman.” Its implementation marks a shift in the trajectory of women's imprisonment and is a material example of the marginalization of gender considerations in public policy.
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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 | 0.028 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.089 | 0.067 |
| Scholarly communication | 0.023 | 0.007 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".