The Role of Psychopathic Personality Disorder in Violence Risk Assessments Using the HCR-20
Why this work is in the frame
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Bibliographic record
Abstract
Antisocial and psychopathic traits are essential to evaluate when assessing risk for violence using the HCR-20. The role of the PCL-R on the HCR-20 was investigated using a series of meta-analytic tests. Across 34 samples in which both tools were rated, AUCs for violence were similar (∼.69), and exclusion of the psychopathy item (H7) did not reduce the HCR-20's accuracy. Quantitative synthesis of results from multivariate analyses conducted in 7 raw datasets that used both tools demonstrated that the average probability of observing violence for every point increase on the HCR-20 (without H7), while controlling for the PCL-R, was 23%, whereas for the PCL-R it was -1%. The HCR-20 (without H7) added incremental validity to the PCL-R, whereas the converse was not true, and only the HCR-20 (without H7) possessed unique predictive validity. Results suggest the HCR-20's predictive validity was not negatively impacted by excluding the PCL-R. Areas for future study are discussed, including research on various ways to assess and incorporate into risk assessment personality traits related to violence.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it