A Latent Class Analysis of the Antisocial Attitudes Domain of the Youth Level of Service/Case Management Inventory
Bibliographic record
Abstract
Antisocial attitudes are a strong predictor of reoffending and frequently incorporated into risk assessment tools, including the Youth Level of Service/Case Management Inventory (YLS/CMI). However, YLS/CMI Attitudes/Orientation domain items appear to cover different issues—antisocial attitudes and willingness to engage in treatment—which have different implications for case management and service provision. Latent Class Analysis of data from 798 Canadian youth probationers identified four classes based on item endorsement on the Attitudes/Orientation domain: High Overall Attitude Needs (19%), Predominantly Antisocial Attitude Items (20%), Predominantly Lack of Service Engagement (9%), and Low Overall Attitude Needs (52%). Class differences were found on index offense, criminogenic needs, and recidivism, with the High Overall Attitude Needs class presenting as most “negative,” followed by Predominantly Antisocial Attitude Items, Predominantly Lack of Service Engagement, and Low Overall. Understanding attitudes based on this class conceptualization can assist probation officers in targeting services more effectively to justice-involved youth.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".