Evaluating the utility of ‘strength’ items when assessing the risk of young offenders
Bibliographic record
Abstract
There is emerging recognition that positive or pro-social characteristics may lessen criminal propensity. There are now several adult and youth forensic instruments that include protective or strength components. Yet evidence supporting the protective capacities of these instruments with youth offending populations is still developing. This study aimed to identity the prevalence of strength items on the Youth Level of Service/Case Management Inventory tool, and their relationships with risk and re-offending for a cohort of 212 multi-cultural Australian juveniles in custody. The prevalence of strengths in the sample was low, and differed by cultural group. Young people who possessed a strength yielded lower instrument total and domain scores and were more likely to be afforded a lower level of risk compared to youth without a strength. Moreover, youth who possessed a strength were significantly more likely to desist from re-offending. This association remained after controlling for level of risk. Findings point to the importance of strengths when assessing a young person’s risk for re-offending.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 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) | 0.002 | 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".