Investigation of the criminal and conditional release profiles of Canadian federal offenders as a function of psychopathy and age.
Bibliographic record
Abstract
Using the Psychopathy Checklist-Revised (PCL-R; R. D. Hare, 1991) diagnostic cut-off score of 30, the complete criminal career and community release profiles of 317 Canadian federal offenders (224 low scorers and 93 scoring within the psychopathic range) were investigated. Adult crimes were coded according to age at commission as well as either violent, nonviolent, or nonsexually violent. Changes in performance following release into the community also were examined. Results indicated that offenders scoring within the psychopathic range consistently committed more violent and nonviolent crimes than their counterparts for about three decades, spanning their late adolescence to their late 40s. Numbers of nonviolent criminal offenses committed by high PCL-R scorers declined considerably after age 30 relative to violent offenses, which declined and then rebounded in the late 30s before a major reduction was evidenced. Throughout adulthood, high PCL-R scorers failed during community release significantly faster than did low scorers. Importantly, from a risk management perspective, the release performance of low PCL-R scorers improved with age, whereas the opposite was seen for high scorers. Further, offenders scoring high on the PCL-R did not show a lower charge to conviction ratio with age, suggesting that they may not have been getting better at manipulating the legal system.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".