Evaluating the Predictive Validity of the Youth Level of Service/Case Management Inventory (YLS/CMI) Risk Assessment Tool for Indigenous and Non-Indigenous Youth
Bibliographic record
Abstract
There is limited predictive validity research available on the Youth Level of Service/Case Management Inventory (YLS/CMI) with Indigenous youth. The current study completed YLS/CMI discrimination and calibration analyses on a randomly selected sample of justice-involved Indigenous and non-Indigenous youth ( N = 1,259). Survival analysis indicated that predictive validity was maintained across groups when using the total score; however, moderate risk Indigenous youth were not more likely to reoffend than low risk Indigenous youth. Area Under the Curve values were significant and large for general and moderate for violent recidivism, although values for time-dependent analyses were somewhat weaker for Indigenous youth. Calibration analyses suggested Indigenous youth were more likely to have a violent re-offense across low and moderate risk levels. While the YLS/CMI demonstrates predictive validity for Indigenous youth, it may underestimate recidivism for Indigenous youth classified as low risk, suggesting greater intervention may be warranted.
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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 | 0.012 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".