Measuring Antisocial Values and Attitudes in Justice-Involved Male Youth
Bibliographic record
Abstract
The Risk–Need–Responsivity (RNR) framework for working with offenders has been well validated. Factors that contribute to reoffending within adult and youth forensic populations have been identified, including antisocial attitudes, but less is known about the measurement of this construct in youth. Thus, in the present study, the reliability and validity of criminal attitudes measures were examined in a sample of justice-involved male youth ( N = 291). Two measures widely used with adult offenders were included in the present study: the Pride in Delinquency Scale (PID) and the Criminal Sentiments Scale–Modified (CSS-M). Both measures were found to be reliable and valid, and of importance, useful in the prediction of reoffending behavior (area under the curve = .70 and .69 respectively). These findings further support the use of the RNR framework in general with youthful offenders, and more specifically, the use of criminal attitudes measures with youth.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".