A comprehensive examination of the psychometric properties of the Hare Psychopathy Checklist-Revised in a Canadian multisite sample of indigenous and non-indigenous offenders.
Bibliographic record
Abstract
The present study examined the psychometric properties of Hare Psychopathy Checklist-Revised (PCL-R; Hare, 2003) scores in a multisite sample of 1,163 federally incarcerated Canadian indigenous and non-indigenous offenders from the Prairie Region of the Correctional Service of Canada. The research occurred against the backdrop of the Ewert v. Canada (2015) matter, in which the PCL-R was originally impugned in Federal Court for use with indigenous persons (later overturned in Canada v. Ewert, 2016). Indigenous men scored higher than non-indigenous men on most components of the PCL-R and had higher rates of recidivism, irrespective of follow-up. Discrimination analyses, however, supported the predictive efficacy of PCL-R total, factor, and facet scores for violent and general recidivism across both ancestral groups, with most group differences in area under the curve (AUC) magnitudes being small and nonsignificant. Calibration analyses demonstrated that higher PCL-R scores were associated with higher rates of general and violent recidivism for both ancestral groups, although higher recidivism rates were observed and estimated for indigenous men at specific PCL-R score thresholds. Confirmatory factor analyses supported the 4-factor model of psychopathy and hence, structural invariance, of PCL-R scores across ancestral groups. Structural equation modeling affirmed the predictive efficacy of the 4-factor model for recidivism. We discuss these findings in terms of clinical applications of the PCL-R and the psychopathy construct in general, with male offenders of indigenous ancestry. (PsycINFO Database Record
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| 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".