Psychopathy, self-identified race/ethnicity, and nonviolent recidivism: A longitudinal study.
Bibliographic record
Abstract
Psychopathy has long been noted to play an important role in the prediction of criminal behavior and offending. Although many studies have demonstrated that psychopathic traits are predictive of violent recidivism among offenders, relatively few studies have examined the predictive validity of psychopathic traits for nonviolent recidivism and very few have examined this issue in a sample of offenders in the United States. To address this issue, we examined the predictive validity of psychopathy for both nonviolent and general recidivism using the Psychopathy Checklist-Revised (PCL-R) in a sample of 422 county jail inmates. We also examined whether ratings on Factors 1 and 2 differentially predicted nonviolent and general recidivism and whether predictive validity varied among European American, African American, and Latino American male offenders. Psychopathic traits were modestly predictive of nonviolent and general (total) recidivism. Factor 2 ratings were not more predictive of nonviolent recidivism than Factor 1 ratings in this sample, but the two factor scores also predicted nonviolent recidivism interactively. Psychopathic traits were also predictive of both outcomes in subsamples of European American and African American offenders, but not among Latino American offenders. Findings are consistent in magnitude and pattern with prior studies addressing the prediction of violence, and they show that the relationship between psychopathy and criminal conduct generalizes to the prediction of nonviolent crime in a United States offender sample. Results suggest potential differences between the predictive validity of psychopathy among Latino American offenders and other racial/ethnic groups, which suggest the need for additional research. (PsycINFO Database Record (c) 2018 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".