Predictive Validity of the Youth Level of Service/Case Management Inventory with Youth who have Committed Sexual and Non-Sexual Offenses
Bibliographic record
Abstract
The predictive validity of the Youth Level of Service/Case Management Inventory (YLS/CMI) and the use of professional override were examined in a matched sample of youth who committed sexual ( n = 204) and non-sexual ( n = 185) offenses. Based on the actuarial score, the YLS/CMI obtained moderate to strong levels of predictive validity for non-violent, violent, sexual, and technical recidivism in both samples of youth. Probation officers always used override to increase risk level classification and did so at a high level for both sexual ( n = 151; 74.0%) and non-sexual ( n = 77; 41.6%) offending youth. There was a detrimental impact on the predictive validity of the YLS/CMI for youth who received an override adjustment, regardless of offending category. These preliminary findings suggest that the application of override should be carefully considered on instruments such as the YLS/CMI.
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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 | 0.001 |
| 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".