The Use of Psychopathy Assessments in Canadian Case Law: A Quantitative and Qualitative Survey of Court Records from 1980-2023
Bibliographic record
Abstract
This study investigated the use of psychopathy assessments in Canadian courts between 1980 and 2023. We reviewed 3,315 court cases and found that psychopathy assessments are used in two distinct ways, either as a clinical measure of psychopathy or exclusively for risk assessment purposes. Psychopathy assessments are most commonly introduced in court by prosecutors, with the Hare Psychopathy Checklist–Revised (PCL-R) being the most frequently used tool. The use of psychopathy assessments increased 858% from the year 2000 to its peak in 2013, followed by a 10-year gradual (63%) decline. There was evidence of adversarial allegiance where prosecution-retained experts gave defendants higher PCL-R scores (d = 1.08) compared to defense-retained experts. PCL-R assessments showed poor reliability when comparing paired scores between prosecution and defense experts, suggesting high risk of Type 1 and 2 errors. Intraclass correlation coefficient (ICC2,1) between prosecution- and defense-retained experts was .079 (95% CI [-0.12, 0.34]), where 40% of experts had a ≥5.9 points scoring difference. A qualitative analysis of 183 expert testimonies on the perceived forensic risk and treatment prospects associated with psychopathy showed significant variability in expert testimonies. Most experts linked psychopathy to high risk of recidivism (72.36%) and described it as a categorically untreatable condition (50.63%), where many experts also stated that treatment makes psychopathic persons worse (15.82%). These findings suggest that some expert testimonies on psychopathy are not aligned with the empirical research. We discuss the potential implications for legal practitioners and comment on the future role of psychopathy assessments in Canadian courts.
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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 | 0.006 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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 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".