Recidivism Risk Assessment for Aboriginal Males: A Brief Review of the Scientific Literature
Bibliographic record
Abstract
No level of violent recidivism is acceptable to Correctional Service of Canada staff or the Canadian public. Among other tools, CSC staff use counselling, supervision, education, and treatment programs to ensure the safe community reintegration of eligible offenders. The core method of determining risk for recidivism is an actuarially-based risk assessment instrument. The general process of contemporary risk assessment is outlined in this paper revealing a number of efficient and effective measures suitable for all male offender populations. Theory and research are reviewed showing that established risk prediction factors such as age, criminal history, anti-social peers, anti-social attitudes, and substance abuse predict criminal recidivism for all offenders regardless of cultural, racial, or geographic heritage. The majority of these validated risk assessment instruments have moderate predictive power for all male offenders. Seven of these instruments are individually reviewed with regard to their use with Aboriginal groups. This paper concludes with recommendations for further research on risk assessment among cultural groups.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".