Comparing predictive validity of Youth Level of Service/Case Management Inventory scores in Indigenous and non-Indigenous Canadian youth.
Bibliographic record
Abstract
OBJECTIVE: There is an increasing recognition of the necessity to establish the predictive validity of risk assessment scores within specific population subgroups, particularly those (including Indigenous peoples) who are overrepresented in the criminal justice system. I compared measures of discrimination and calibration of the Youth Level of Service/Case Management Inventory (YLS/CMI) in Indigenous and non-Indigenous youth probationers in Ontario, Canada. HYPOTHESES: Compared with non-Indigenous youth, Indigenous youth would have higher risk scores and reoffense rates. The YLS/CMI would predict reoffending and time to reoffense significantly and comparably for Indigenous and non-Indigenous youth, but there would be group difference discrimination (sensitivity, specificity) and calibration (positive predictive value, negative predictive value). METHOD: Justice ministry-supplied data on 400 Indigenous and non-Indigenous youth (330 male, 70 female) individually matched on key background variables were analyzed to provide measures of discrimination and calibration of the YLS/CMI, with 3-year recidivism as the primary outcome. RESULTS: = .60); 70% of Indigenous youth and 46% of non-Indigenous youth reoffended (ϕ = .24). Overall measures of discrimination (area under the curve) and calibration (logistic regression) were significant and did not differ across groups. Cross-area under the curve results indicated that the YLS/CMI discriminated Indigenous recidivists from non-Indigenous nonrecidivists but differentiated Indigenous nonrecidivists from non-Indigenous recidivists at chance level. In addition, recidivism was underestimated for low-risk Indigenous youth compared with non-Indigenous youth, but specificity was also low; only 28% of Indigenous youth who did not reoffend were assessed as low risk. Results were largely consistent across male and female youth. CONCLUSIONS: Examining subgroup predictive validity using multiple indices provides important information that should inform policy and practice discussions regarding fair use of risk assessment tools. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".