Communal Narcissism and Sadism as Predictors of Everyday Vigilantism
Bibliographic record
Abstract
Vigilantes monitor their social environment for signs of wrongdoing and administer unauthorized punishment on those who they perceive to be violating laws, social norms, or moral standards. We investigated whether the willingness to become a vigilante can be predicted by grandiose self-perceptions about one's communality (communal narcissism) and enjoyment of cruelty (sadism). As hypothesized, findings demonstrated both variables to be positively related to becoming a vigilante as measured by reports of past and anticipated vigilante behavior (Study 1) and by dispositional tendencies toward vigilantism (Studies 1 and 2). We also found communal narcissism and sadism predicted the perceived effectiveness of vigilante actions exhibited by others (Study 2) and the intention to engage in vigilantism after witnessing a norm violation (Study 3). Finally, Study 3 also demonstrated that the tendency for communal narcissists and sadists to become a vigilante might vary based on the expected consequences of the observed norm violation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".