Classification without Validity or Equity: An Empirical Examination of the Custody Rating Scale for Federally Sentenced Women Offenders in Canada
Bibliographic record
Abstract
In order to fulfil its legal mandate to assign an initial security classification of minimum, medium, or maximum to all federally sentenced women offenders, the Correctional Service of Canada (CSC) has used the Custody Rating Scale (CRS) - an objective statistical tool - for more than a decade. Despite CSC's numerous claims of this tool's validity and the equity of its outcomes, it has been repeatedly suggested that the CRS misclassifies women in general, and Aboriginal women in particular. This article extends the (theoretical) debate surrounding the applicability of the CRS for these two sub-groups of the inmate population. Using actual findings published by CSC, this article empirically demonstrates that the overall scale, one of its two sub-scales, and many of the individual items making up the classification tool have weak or no predictive validity for Aboriginal and/or non-Aboriginal women. Further, it provides evidence that the CRS introduces a systematic bias against Aboriginal (relative to non-Aboriginal) offenders whereby a substantial proportion of these minority women are unjustly over-classified in higher levels of security. The article concludes with a discussion of several of the broader theoretical and policy implications of these findings.
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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 | 0.022 | 0.116 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".