Risk Metrics and the Five-Level System for the Youth Level of Service/Case Management Inventory in Two Samples of Canadian Justice System Involved Youth
Bibliographic record
Abstract
Comparing results across risk assessment tools is challenging due to varying methodologies. Implementing the Five-Level system in adult tools shows promise, but it has not been examined with youth assessments. Using a forensic and a community sample of justice-involved youth in Ontario, Canada, we calculated risk metrics–percentiles, risk ratios, and recidivism rates–separately, by gender, for the Youth Level of Service/Case Management Inventory (YLS/CMI). Next, we implemented the Five-Level system and preliminarily examined its construct validity. YLS/CMI score distributions and recidivism rates varied across samples. While rates varied across risk categories, only one risk ratio was significant; Low risk community-probation young men were 68% less likely to reoffend than Moderate risk community-probation young men. There were significant pairwise differences in YLS/CMI scores across all risk levels generated using the Five-Level system, with some differences in psychopathy, aggression, and pride in delinquency scores across levels.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".