Assessing juvenile offenders: Preliminary data for the Australian adaptation of the youth level of service/case management inventory (Hoge & Andrews, )
Bibliographic record
Abstract
*Some of the psychometric results were presented by Thompson, A. P., & Pope, Z. (2003). The conceptual and psychometric basis for risk – need assessment in juvenile justice. In M. Katsikitis (Ed.), Proceedings of the 38th APS Annual Conference (pp. 224 – 228). Melbourne: The Australian Psychological Society.The developmental phase and preliminary psychometric data are reported for an Australian adaptation of an assessment inventory for juvenile offenders. Specifically, the Australian Adaptation of the Youth Level of Service/Case Management Inventory (YLS/CMI-AA, Hoge, & Andrews, Citation1995) is used to assess risks, needs and strengths to inform decision making with juvenile offenders. Data from a sample of 290 juvenile offenders were used to analyse item and score characteristics which, with few exceptions, performed in keeping with traditional psychometric standards. Predictive validity in a subsample of 174 males followed for recidivism between 6 and 32 months resulted in a correlation of 0.28 and area under the receiver operating characteristic (ROC) curve of 0.67 for the total score on the inventory. The results and use of the inventory are placed in the context of related developments in other jurisdictions.
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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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".