How reliable are Psychopathy Checklist–Revised scores in Canadian criminal trials? A case law review.
Bibliographic record
Abstract
The Psychopathy Checklist-Revised (PCL-R; Hare, 2003) is a professional rating scale that enjoys widespread use in forensic and correctional settings, primarily as a tool to inform risk assessments in a variety of types of cases (e.g., parole determinations, sexually violent predator [SVP] civil commitment). Although widely described as "reliable and valid" in research reports, several recent field studies have suggested that PCL-R scores provided by examiners in forensic cases are significantly less reliable than the interrater reliability values reported in research studies. Most of these field studies, however, have had small samples and only examined SVP civil commitment cases. This study builds on existing research by examining the reliability of PCL-R scores provided by forensic examiners in a much more extensive sample of Canadian criminal cases. Using the LexisNexis database, we identified 102 cases in which at least 2 scores were reported (of 257 total PCL-R scores). The single-rater intraclass correlation coefficient (ICC(A1)) was .59, indicating that a large percentage of the variance in individual scores was attributable to some form of error. ICC values were somewhat higher for sexual offending cases (.66) than they were for nonsexual offending cases (.46), indicating that poor interrater reliability was not restricted specifically to the assessment of sexual offenders. These and earlier findings concerning field reliability in legal cases suggest that the standard error of measurement for PCL-R scores that are provided to the courts is likely to be much larger than the value of 2.90 reported in the instrument's manual.
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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 | 0.045 | 0.179 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.029 | 0.032 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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 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".