A cluster analytic examination and external validation of psychopathic offender subtypes in a multisite sample of Canadian federal offenders.
Bibliographic record
Abstract
The present study is a cluster analytic examination and validation of psychopathic offender subtypes from 4 combined samples of Canadian federally incarcerated offenders, most of whom were serving sentences for violent offenses. The men were rated on the Hare Psychopathy Checklist-Revised (PCL-R; Hare, 1991, 2003) on the basis of comprehensive file information and 314 cases were extracted using a PCL-R total cut score of 25. Cluster analysis of the 4 PCL-R facets converged at a 2-cluster solution: a primary subtype characterized by prominent interpersonal and affective features of psychopathy and a secondary subtype characterized by comparatively few interpersonal features and high scores on the remaining facets. Validation analyses found that the vast majority of primary psychopathic offenders (74.1%) were White or of non-Aboriginal descent in contrast to the secondary subtype (47.6%). Secondary psychopathic offenders tended to be actuarially higher risk, have greater criminogenic needs, and to make greater amounts of treatment change on criminogenic targets; however, contrary to expectations, within-treatment changes from a violence reduction program were significantly associated with reductions in violent recidivism for primary, but not secondary, variants. There were few differences in rates of recidivism between the groups overall; secondary variants had higher rates of sexual violence which was largely accounted for by individual differences in baseline static risk. Implications for risk assessment, treatment planning, and the classification and etiology of primary and secondary psychopathy are discussed.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".