Psychopathy and the predictive validity of the PCL-R: an international perspective
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Its controversial past notwithstanding, psychopathy has emerged as one of the most important clinical constructs in the criminal justice and mental health systems. One reason for the surge in theoretical and applied interest in the disorder is the development and widespread adoption of reliable and valid methods for its measurement. The Hare PCL-R provides researchers and clinicians with a common metric for the assessment of psychopathy, and has led to a surge in replicable and meaningful findings relevant to the issue of risk for recidivism and violence, among other things. Most of the research thus far has been based on North American samples of offenders and forensic psychiatric patients. We summarize this research and compare it with findings from several other countries, including England and Sweden. We conclude that the ability of the PCL-R to predict recidivism, violence, and treatment outcome has considerable cross-cultural generalizability, and that the PCL-R and its derivatives play a major role in the understanding and prediction of crime and 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| 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 it