Using the PCL-R to Help Estimate the Validity of Two Self-Report Measures of Psychopathy With Offenders
Bibliographic record
Abstract
Two self-report measures of psychopathy, Levenson's Primary and Secondary Psychopathy scales (LPSP) and the Psychopathic Personality Inventory (PPI), were administered to a large sample of 1,603 offenders. The most widely researched measure of criminal psychopathy, the Hare Psychopathy Checklist-Revised (PCL-R), served as a provisional referent for estimating the construct validity of these self-report measures with offenders. Compared with the LPSP, the PPI displayed higher zero-order correlations with the PCL-R, better convergent and discriminant validity, and more consistent incremental utility in predicting PCL-R scores. Furthermore, using a variant of Westen and Rosenthal's approach to evaluating the construct validity of a new measure, compared with the LPSP, the PPI's pattern of associations with measures of 35 external criterion variables was more similar to the pattern observed for the PCL-R. Results generally provide stronger support for the validity of the PPI than the LPSP in offender populations using the PCL-R as a provisional benchmark, particularly for assessing interpersonal and affective features of psychopathy.
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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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".