The Psychopathy Checklist: Youth Version and adolescent and adult recidivism: Considerations with respect to gender, ethnicity, and age.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The present study investigated the predictive accuracy of the Psychopathy Checklist: Youth Version (PCL: YV; A. E. Forth, D. S. Kosson, & R. D. Hare, 2003) for youth and adult recidivism, with respect to gender, ethnicity, and age, in a sample of 161 Canadian young offenders who received psychological services from an outpatient mental health facility. The PCL: YV significantly predicted any general, nonviolent, and violent recidivism in the aggregate sample over a 7-year follow-up; however, when results were disaggregated by youth and adult outcomes, the PCL: YV consistently appeared to be a stronger predictor of youth recidivism. The PCL: YV predicted youth recidivism for subsamples of female and Aboriginal youths, and very few differences in the predictive accuracy of the tool were observed for younger vs. older adolescent groups. Both the 13-item (i.e., D. J. Cooke & C. Michie, 2001, 3-factor) and the 20-item (i.e., R. D. Hare, 2003, 4-factor) models appeared to predict various recidivism criteria comparably across the aggregate sample and within specific demographic subgroups (e.g., female and Aboriginal youth). The Antisocial facet contributed the most variance in the prediction of adult outcomes, whereas the 3-factor model contributed significant incremental variance in the prediction of youth recidivism outcomes. Potential implications concerning the use of the PCL: YV in clinical and forensic assessment contexts are discussed.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it