Examining the economic costs of crime associated with psychopathic personality disorder: A reply to Verona and Joyner (2022).
Bibliographic record
Abstract
In our article, "How much does that cost? Examining the economic costs of crime in North America attributable to people with psychopathic personality disorder" (Gatner et al., 2023, pp. 391-400), we estimated that psychopathic personality disorder (PPD) was associated with substantial crime costs, using a top-down approach of national costs in the United States and Canada. Verona and Joyner (2023) raised several concerns about our findings. Although we think some of their points help to map directions for future research, we disagree with others they raised related to the conceptualization of PPD, the problem of undetected crimes, and their concerns with putative national comparisons. We strongly welcome debate about the societal impacts of PPD in the hope that it spurs increased attention and innovation regarding the treatment and management of PPD. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".