Validation of the youth level of service/case management inventory with Saskatchewan young offenders
Bibliographic record
Abstract
The Youth Level of Service/Case Management Inventory (YLS/CMI), an instrument developed by Hoge and Andrews (1996a; 1996b), was designed to determine risk of recidivism for young offenders. The YLS/CMI's development was based on the Personal, Interpersonal, and Community-Reinforcement theory (Andrews & Bonta, 1998) which proposes that antisocial attitudes, associates, behavioral history, and personality are the most important predictors of criminal behavior. The current investigation examined the predictive validity of the YLS/CMI in predicting recidivism for Saskatchewan Young Offenders as well as investigating the roles of antisocial attitudes, associates, criminal history, and personality in predicting future criminal behavior for this population. Further, the study explored the effectiveness of the YLS/CMI to predict recidivism for Aboriginal young offenders as well as for male and female young offenders. Relationships among the YLS/CMI composite score and subscale scores and the two measures of recidivism were investigated through correlations. Results revealed significant positive relationships between the composite and subscale scores and both outcome measures with some differences within the subgroups noted. Results also indicated that criminal history, negative peers and antisocial attitudes were predictive of reoffending behavior. Antisocial personality, however, was not predictive of either measure of recidivism. Overall, it was concluded that the YLS/CMI demonstrated adequate predictive ability with Saskatchewan Young Offenders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".