Research and Clinical Scoring of the Psychopathy Checklist can show good Agreement
Bibliographic record
Abstract
The Hare Psychopathy Checklist (PCL-R) is an important predictor of violent behavior and is widely used to make important decisions about forensic clients. Some research casts doubt on whether the scoring of the PCL-R in clinical practice matches that attained in research and, therefore, whether the use of the PCL-R is warranted in high-stakes decisions. We examined scoring correspondence of the PCL-R in 58 offenders where scoring by trained clinicians was compared with that by a very experienced researcher whose scoring was of known predictive validity (or with a student supervised by this experienced researcher). Research and clinical scorers showed good agreement (Spearman’s rank order correlation = .85; intraclass correlation coefficient = .79, absolute agreement for single measures), especially on those parts of the PCL-R that are most consistently and robustly associated with violence. We conclude that trained clinicians can achieve acceptable reliability and validity when scoring the PCL-R, especially for risk assessment.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".