Predicting Recidivism of Aboriginal Youth Offenders: A Look at an Established Risk Assessment Tool and Culturally-Specific Predictors
Bibliographic record
Abstract
The application of standard risk assessment tools with Aboriginal youth offenders has been a highly controversial practice. Criticisms are premised on the fact that risk/need tools are largely founded on the social and historical experiences of non-Aboriginal offenders. In turn, scholars and practitioners have recommended the use of culturally- specific risk/need factors considering Aboriginal culture and the unique context of Aboriginal people in Canada. The current project consists of two studies designed to contribute to our understanding of these concerns. Study 1 examined the predictive validity (both discrimination and calibration) of the YLS/CMI with both Aboriginal and non-Aboriginal youth offenders. Results found that although the YLS/CMI provides adequate discrimination for Aboriginal offenders (AUCs from .555 to .606), it underestimates the absolute recidivism rates of low and moderate risk Aboriginal youth compared to non-Aboriginal youth. Study 2 explored the utility of PSRs as sources of culturally-specific information and examined the predictive validity of those factors included. Results indicate that although a number of culturally-specific factors predicted re-offending, particularly family breakdown and community variables, PSRs are an inconsistent source of this information. Overall, the findings suggest that the predictive validity of the YLS/CMI with Aboriginal offenders may be improved with increased focus on family breakdown and home community. Implications and next steps for both practice and research are discussed.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".