Short- and long-term recidivism prediction of the PCL-R and the effects of age: A 24-year follow-up.
Why this work is in the frame
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Bibliographic record
Abstract
We prospectively examined the short- and long-term prediction of several recidivism outcomes as a function of psychopathy and age in a sample of 273 Canadian federal inmates with an average 24 years post-release follow-up. Offenders were rated using the original 22-item Hare Psychopathy Checklist (PCL: Hare, 1980) based on extensive archival file information, and the ratings were used to compute the Psychopathy Checklist-Revised (Hare, 2003) and the 4 facet scores. PCL-R total scores and the Lifestyle and Antisocial facets, but not the Interpersonal and Affective facets, showed mostly small and some moderate predictive efficacy for general and nonviolent recidivism over 3-, 5-, 10-, and 20-year fixed follow-ups, and predicted violence recidivism at shorter follow-ups. Age at release was negatively correlated with all recidivism outcomes and follow-up periods for both high and low PCL-R rated offenders, and uniquely predicted all recidivism outcomes after controlling for the PCL-R using Cox regression survival analysis. Increased age was consistently linked to recidivism reduction even for psychopathic offenders. The results showed that both PCL-R scores and age contributed to the prediction of recidivism; however, the PCL-R facets made differential contributions that varied with the type of offense (violent vs. nonviolent) and follow-up time (shorter vs. longer). The results have implications for both risk assessment using the PCL-R and potentially for risk reduction interventions.
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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 | 0.002 | 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 | 0.002 |
| 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 it