Identifying vulnerability to violence: the role of psychopathy and gender
Bibliographic record
Abstract
Purpose Recent research has suggested that a heightened sensitivity to nonverbal cues may give individuals with psychopathic traits an advantage when selecting potential victims. The purpose of this paper is to examine the effect of gender on the association between psychopathy and perceptions of vulnerability to violent victimization. Design/methodology/approach A sample of 291 undergraduate students viewed a series of eight videos depicting individual female targets walking down a hallway from behind. Participants rated each target’s vulnerability to violent victimization and provided a justification for each rating. In addition to these ratings, participants completed the Self-Report Psychopathy Scale. Findings A series of hierarchical linear regressions revealed gender differences in the association between psychopathy and accuracy. Among male observers, total psychopathy scores, Factor 2 psychopathy scores, and scores on the antisocial behavior facet were positively associated with accuracy in perceiving vulnerability to violent victimization. Conversely, no associations were identified between psychopathy (total, Factors, and facets) and accuracy among female observers. This suggests that the adept ability to accurately perceive nonverbal cues signalling vulnerability is specific to males exhibiting psychopathic traits. Originality/value The results of the current study highlight the importance of distinguishing male and female psychopathy in research and practice. Moreover, with an understanding of individual differences in the ability to accurately perceive nonverbal cues associated with vulnerability, we may begin to develop intervention strategies aimed at reducing future incidences of victimization.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".