Predictive and Convergent Validity of the Youth Assessment and Screening Instrument in a Sample of Male and Female Justice-Involved Youth
Bibliographic record
Abstract
Sufficient evidence exists that gender should and does matter in offender management. This study examined the predictive validity of risk and strength factors extracted from the Youth Assessment and Screening Instrument (YASI) and the Youth Level of Service/Case Management Inventory (YLS/CMI) in a sample of 254 justice-involved youth (148 males, 106 females) from Ontario, Canada. Overall, total risk scores from both measures predicted recidivism (area under receiver operator characteristic curve [AUCs] = .62-.70). Domain-level analyses illustrated that criminal attitudes and associates (scored as risks or protective/strengths) were among the strongest predictors of recidivism in both genders. The YASI demonstrated strong convergent validity with the YLS/CMI. The results support the YASI and the YLS/CMI as viable risk assessment measures for justice-involved male and female youth. Given that the YASI includes both gender neutral and gender responsive items, it may be a particularly good choice for use with justice-involved females.
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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".