Field Inter-Rater Reliability of the Psychopathy Checklist–Revised
Bibliographic record
Abstract
Strong inter-rater reliability has been established for the Hare Psychopathy Checklist-Revised (PCL-R), specifically by examiners in research contexts. However, there is less support for inter-reliability in applied settings. This study examined archival data that included a sample of sex offenders ( n = 178) who entered federal custody between 1992 and 1998. The offenders were assessed using the PCL-R on two occasions. The first assessment occurred at Millhaven Institution, the intake unit for federally incarcerated offenders in the province of Ontario. The second assessment took place upon inmates' transfer to the Regional Treatment Center, which admits federal inmates with intense psychological and psychiatric needs. Intra-class correlation coefficients (ICCs) were calculated for item, total, factor, and facet scores. The ICC absolute agreement for the PCL-R total and factor scores from raters across both settings was slightly better than what has been previously reported by Hare. Results of this study show that the reliability of PCL-R scores in field settings can be comparable to those in research settings. Authors conclude by highlighting the importance of training, consultation, considering different scores for a given item, following the guidelines of the manual in addition to considering measures that enhance neutrality and reliability of findings in the criminal justice system.
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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 | 0.050 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".