Behavioral Confirmation of Everyday Sadism
Bibliographic record
Abstract
Past research on socially aversive personalities has focused on subclinical psychopathy, subclinical narcissism, and Machiavellianism-the "Dark Triad" of personality. In the research reported here, we evaluated whether an everyday form of sadism should be added to that list. Acts of apparent cruelty were captured using two laboratory procedures, and we showed that such behavior could be predicted with two measures of sadistic personality. Study 1 featured a bug-killing paradigm. As expected, sadists volunteered to kill bugs at greater rates than did nonsadists. Study 2 examined willingness to harm an innocent victim. When aggression was easy, sadism and Dark Triad measures predicted unprovoked aggression. However, only sadists were willing to work for the opportunity to hurt an innocent person. In both studies, sadism emerged as an independent predictor of behavior reflecting an appetite for cruelty. Together, these findings support the construct validity of everyday sadism and its incorporation into a new "Dark Tetrad" of personality.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".