Thinking outside the box: Reducing administrative segregation with Indigenous prisoners
Bibliographic record
Abstract
This project looks at how correctional policy reforms in the near term can reduce admissions of Indigenous prisoners to administrative segregation in Canadian penitentiaries. In a given year, approximately one-third of Indigenous prisoners will spend time in segregation. While the federal government has introduced a bill to try to address the problematic aspects of the practice, Indigenous prisoners continue to suffer disproportionate impacts on correctional outcomes and rehabilitation as a result of their overrepresentation. This is supported by the BC Supreme Court ruling in BC Civil Liberties Association v. Canada (AG), Correctional Service Canada statistics, and by experts interviewed in this study. Drawing on a review of the literature, a scan of correctional systems in Australia, New Zealand, and the United States, and semi-structured qualitative elite interviews, three non-mutually exclusive policy options are explored. Through analysis of these sources, criteria for success are derived and the formation of an independent review panel is recommended in the near term. A secondary option to expand eligibility for Pathways Initiatives is also discussed, as well as longer-term considerations that fall out of scope of this project.
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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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".