The Classification of Federally Sentenced Women in Canada: Addition of Gender-Informed Variables to the Custody Rating Scale Contributes Incremental Predictive Validity
Bibliographic record
Abstract
The Correctional Service of Canada (CSC) uses the Custody Rating Scale (CRS) for initial security classification; it is gender-neutral. Gender-informed scholars contend that gender-neutral assessments are problematic for use with justice-impacted women, as they exclude factors (e.g., victimization) deemed more relevant for women. Using an archival database with 1,555 federally sentenced women in Canada, we examined the extent that gender-informed indicators could yield incremental predictive validity (predicting institutional misconduct) beyond the CRS. Specifically, gender-informed variables from these domains were tested: mental health, substance misuse, relationship dysfunction, personal/emotional difficulties, parental/family issues, and victimization. Results revealed at least one gender-informed variable from each domain significantly predicted institutional misconducts. Composite gender-informed scales were created from the set of significant gender-informed predictors. Area under the curve (AUC) and hierarchical Cox regression analyses revealed the composite gender-informed scales contributed incremental predictive validity above and beyond the CRS. Although the CRS was predictive, it can be improved by including gender-informed variables.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".