Race, Gender, and Risk Assessments in Canadian Federal Prison
Bibliographic record
Abstract
In Canada, all federally incarcerated individuals are required to complete a number of actuarial risk assessments upon entering prison which influence the security level in which they are housed, opportunities to participate in rehabilitative services while incarcerated, and prospects for parole. While proponents of actuarial risk assessments—which make algorithmic decisions based on objective inputs—argue that such tools can reduce the influence of racial and gender bias in carceral decision making, others argue that they may perpetuate or exacerbate racial and gender inequality. The extent to which racial and gender disparities exist in the outcomes of the actuarial risk assessments used in federal Canadian prisons is largely unknown. Using newly available data, we characterize racial and gender disparities in the outcomes of actuarial risk assessments used in Canadian prisons and their relationship to outcomes. We find significant racial differences in risk assessment scores that leave Black and Indigenous Canadians worse off than their white counterparts, important differences for all racial groups in the treatment of women, and evidence suggestive of racial bias in parole and housing decisions.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".