Psychopathic traits in a large community sample: Links to violence, alcohol use, and intelligence.
Bibliographic record
Abstract
Numerous studies conducted with offender or forensic psychiatric samples have revealed that individuals with psychopathic traits are at risk for violence and other externalizing psychopathology. These traits appear to be continuously distributed in these samples, leading investigators to speculate on the presence of such traits in the general population. Nonetheless, few studies of psychopathy have been conducted with large random samples of individuals from the community. The community sample from the MacArthur Violence Risk Assessment Study provides an opportunity to examine the prevalence and structural nature of psychopathic traits, as well as their association with external correlates in an urban community. The community data (N = 514) represent a stratified random sample of persons between the ages of 18 and 40 who were assessed on the Psychopathy Checklist: Screening Version (PCL: SV) and also for violent behavior, alcohol use, and intellectual functioning. Structural equation model analyses revealed that a 4-factor model found in offender and forensic psychiatric samples fit the community data well and was invariant across sex and ethnicity. Also, a superordinate factor comprehensively accounted for the 4 psychopathy first-order factors and significantly predicted the external correlates. The findings offer insight into the dimensional nature of the psychopathy construct.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".