Predictive Properties of a General Risk-Need Measure in Diverse Justice Involved Youth: A Prospective Field Validity Study
Bibliographic record
Abstract
The current investigation was a prospective field validity study examining the discrimination and calibration properties of a general risk-need tool (Level of Service Inventory–Saskatchewan Youth Edition [LSI-Sk]) in a diverse sample of 284 court adjudicated youths, rated by their youth workers on the measure and followed up an average of 9.3 years. The overall risk level and need total demonstrated moderate predictive accuracy for general, violent, and nonviolent recidivism in the aggregate sample, although area under the curve (AUC) magnitudes fluctuated among gender and Indigenous ethnocultural subgroups. Variability in AUC values for the measure’s eight criminogenic need domains further reflected greater salience of certain needs among subgroups. Finally, clinician rated level of gang involvement incrementally predicted recidivism to varying degrees after controlling for overall risk and need. Implications for responsible use of risk assessment tools as part of individualized and gender/ethnoculturally responsive risk assessment practices with youth are discussed.
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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 | 0.008 | 0.023 |
| 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".