The Relationship Between Changes in Dynamic Risk Factors and the Predictive Validity of Risk Assessments Among Youth Offenders
Bibliographic record
Abstract
The Youth Level of Service/Case Management Inventory (YLS/CMI) is a widely used risk assessment tool for youth offenders. It is intended to be administered regularly to capture changes in criminogenic needs and thus inform case management during a youth’s sentence. However, there is a dearth of research examining whether updated assessments are more predictive of recidivism than initial assessments. We examined whether including dynamic risk factors increased the predictive validity of the YLS/CMI and whether changes in dynamic risk scores improved the prediction of recidivism. Two hundred youth offenders were tracked from their first risk assessment conducted at probation to their most recent risk assessment completed prior to first reoffense or study end date. Inclusion of dynamic risk scores improved predictive accuracy above static risk and updated dynamic risk scores improved accuracy over those obtained from the initial assessment, supporting the utility of the YLS/CMI as a reassessment tool.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".