Psychopathy and crime: Testing the incremental validity of PCL-R-measured psychopathy as a predictor of general and violent recidivism.
Bibliographic record
Abstract
It has been argued that psychopathy plays a vital role in the criminal justice system. To test this assumption, the incremental validity of the psychopathy construct was examined in 198 male Canadian prison inmates serving time for nonsexual offenses and 122 male U. S. inmates undergoing forensic evaluations. When these two samples--which had been used previously to test the incremental validity of the four Psychopathy Checklist-Revised (PCL-R: Hare, 2003) facet scores (Walters, Wilson, & Glover, 2011)--were treated as a single group, second-order confirmatory factor analysis and item response theory principles indicated that a three-factor hierarchical model of the PCL-R facets (interpersonal, affective, lifestyle) fit the data better than a four-factor hierarchical model (interpersonal, affective, lifestyle, antisocial). When the two samples were examined separately, a composite of the first three PCL-R facets (interpersonal, affective, lifestyle) failed to predict general and violent recidivism above and beyond the contributions of age and criminal history. These results bring into question the utility of the psychopathy construct, as measured by Facets 1, 2, and 3 of the PCL-R, to predict important criminal justice outcomes like recidivism. Additional research using alternative measures of psychopathy and a wider array of outcome measures is required to determine the extent to which the psychopathy construct contributes to our understanding of criminal behavior.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".