Probative of Prejudice: Procedural Unfairness Underlying Security Threat Group Classifications in Canadian Prisons
Bibliographic record
Abstract
Achieving meaningful oversight and enforcement of prisoners’ rights has long been a challenge for those incarcerated in Canada’s prison system. This is illustrated in part by how the Correctional Service of Canada (CSC) applies the Security Threat Group (STG) classification to Black prisoners. CSC disproportionately classifies Black prisoners as being members or affiliates of STGS–even when those allegations are of unknown reliability. This paper analytically highlights how this practice impacts the liberty interests of these prisoners on the basis of unproven allegations. It also considers how a lack of procedural safeguards in this context contributes to a larger pattern of systemic anti-Black racism within Canadian prisons. This paper argues that requiring STG involvement to be proven beyond a reasonable doubt before an independent decision-maker would serve as an important reform toward addressing this issue. While such a measure would not remedy the systemic nature of anti-Black racism within CSC, it could nevertheless meaningfully reduce instances of unfounded STG classifications for all prisoners, avoid arbitrarily prolonging periods of incarceration, and serve as a basis to expand procedural safeguards in response to other forms of over-securitization.
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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 | 0.011 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.019 | 0.016 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".