What is this measuring? Comment on Gatner et al. (2022).
Bibliographic record
Abstract
In their crime cost estimation, Gatner et al. (2022) conclude that psychopathic personality disorder (PPD) is associated with billions of dollars of crime costs in the United States (US) and Canada. Gatner et al.'s analysis goes far in putting a cost estimate to PPD, when the burden of psychopathy for the criminal justice system has been unspecified for years. Nonetheless, in the present commentary, we identify two broad problems with their analyses that motivate caution in the interpretation of the findings and their potential applicability: (a) the conceptualization of psychopathy that formed the bases for estimates of PPD, and (b) the assumptions underlying crime cost estimates made by Gatner et al. The questionable assumptions and diminished focus on the criminal justice context in the US versus Canada limit the extent to which these estimates can produce useful policy implications and may instead perpetuate misconceptions of crime and PPD. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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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 | 0.011 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.069 | 0.064 |
| Insufficient payload (model declined to judge) | 0.008 | 0.009 |
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".