Short- and long-term recidivism prediction of the PCL-R and the effects of age: A 24-year follow-up.
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.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".