Examining the interrater reliability of the Hare Psychopathy Checklist—Revised across a large sample of trained raters.
Bibliographic record
Abstract
The goal of the current study was to assess the interrater reliability of the Psychopathy Checklist-Revised (PCL-R) among a large sample of trained raters (N = 280). All raters completed PCL-R training at some point between 1989 and 2012 and subsequently provided complete coding for the same 6 practice cases. Overall, 3 major conclusions can be drawn from the results: (a) reliability of individual PCL-R items largely fell below any appropriate standards while the estimates for Total PCL-R scores and factor scores were good (but not excellent); (b) the cases representing individuals with high psychopathy scores showed better reliability than did the cases of individuals in the moderate to low PCL-R score range; and (c) there was a high degree of variability among raters; however, rater specific differences had no consistent effect on scoring the PCL-R. Therefore, despite low reliability estimates for individual items, Total scores and factor scores can be reliably scored among trained raters. We temper these conclusions by noting that scoring standardized videotaped case studies does not allow the rater to interact directly with the offender. Real-world PCL-R assessments typically involve a face-to-face interview and much more extensive collateral information. We offer recommendations for new web-based training procedures. (PsycINFO Database Record
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".