The PCL–R and capital sentencing: A commentary on “Death is different” DeMatteo et al. (2020a).
Bibliographic record
Abstract
DeMatteo et al. (2020a) published a Statement in this journal declaring that the Psychopathy Checklist-Revised (PCL-R) “cannot and should not” be used in U.S. capital-sentencing cases to assess risk for serious institutional violence. Their stated concerns were the PCL-R’s “imperfect interrater reliability,” its “variability in predictive validity,” and its prejudicial effects on the defendant. In a Cautionary Note, we (Olver et al., 2020) raised questions about the Statement’s evaluation of the PCL-R’s psychometric properties, presented new data, including a meta-meta-analysis, and argued that the evidence did not support the Statement’s declaration that the PCL-R “cannot” be used in high stakes contexts. In their reply, titled “Death is Different,” DeMatteo et al. (2020b) concurred with several points in our Cautionary Note, disputed others, asserted that we had misunderstood or mischaracterized their Statement, and dismissed our new data and comments as irrelevant to the Statement’s purpose. This perspective on our commentary is inimical to balanced academic discourse. In this article, we contend that DeMatteo et al. (2020b) underestimated the reliability and predictive validity of PCL-R ratings, overestimated the centrality of the PCL-R in sentencing decisions, and underplayed the importance of other factors. Most of their arguments depended on sources other than capital cases, including mock trials, Sexually Violent Predator (SVP) hearings, and studies that included the prediction of general violence. We conclude that the rationale for the bold “cannot and should not” decree is open to debate and in need of research in real-life venues.
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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 | 0.047 | 0.224 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.019 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.011 | 0.005 |
| Research integrity | 0.092 | 0.092 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".