Risk Metrics and the Five-Level System for the Youth Level of Service/Case Management Inventory (YLS/CMI): A Comparison of Two Samples of Justice-Involved Youth in Canada
Bibliographic record
Abstract
Risk assessments are integral in the criminal justice system for predicting reoffending and guiding treatment. Challenges arise in using diverse risk tools due to difficulties comparing results across instruments with varying risk measurement methodologies. To address this, the Council of State Governments (CSG) Justice Center developed guidelines, applying metrics like percentiles, risk ratios, and recidivism rates to generate five risk levels (Hanson et al., 2017). While these levels have been applied to adult measures, their application to youth assessments remains unexplored. The Youth Level of Service/Case Management Inventory (YLS/CMI) is a widely used youth risk assessment tool, categorizing total scores into Low, Moderate, High, and Very High risk based on percentiles. However, percentiles convey a youth's position without directly linking to reoffense rates or communicating reoffending probability, requiring additional risk communication metrics. This dissertation aimed to provide risk metrics, separately by gender, for the YLS/CMI, with secondary aims to implement the CSG Justice Center’s Five-Level system and derive standardized risk levels. The final objective was to measure construct validity via psychopathy, aggression, and pride in delinquency measures. Using data from two justice-involved youth samples in Ontario, Canada – a probation sample (N = 540) and a forensic assessment sample (N = 880) – the study revealed that approximately half of the sample reoffended within two years, with similar rates in both samples. Percentiles highlighted YLS/CMI score distribution variations, emphasizing context dependence. Risk ratios were notable for Low Risk males in the community sample, indicating a 68% lower likelihood of reoffending than Moderate risk individuals. Recidivism rates varied across YLS/CMI risk categories and sample types, with significant differences in mean total scores observed across the Five-Level system in both samples. Construct validity in the forensic sample demonstrated significant differences in psychopathy, aggression, and pride in delinquency across levels. The discussion encompasses developmental factors, emphasizing the age-crime curve and addressing the distinctive needs of justice-involved youth. Recommendations include extending the validation of the five levels to youth risk tools, establishing local norms, and investigating the Five-Level system’s applicability across diverse settings.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".