Bibliographic record
Abstract
Previous research has demonstrated that disrespect sensitivity plus anger rumination (DSAR) predicts outcomes congruent with sadistic motivation (such as positive affect in response to target harm) in pranking contexts. Because “successful” pranksters often appear giddy rather than overtly hostile, we conducted three studies involving 990 Canadian undergraduates based on the idea that DSAR-related hostility could be operating outside of awareness. When controlling for overlap among humour styles, DSAR predicted greater self-reported use of self-defeating but not aggressive humour (Study 1). Higher DSAR pranksters/observers (but not victims) perceived more happiness than anger in an art interpretation task by default but more anger than happiness when pranks were salient (Study 2). Contrary to their assertions, higher DSAR scorers’ word fragment and projective test responses suggested implicit hostile/dominant tendencies but inhibition of overt aggression in the neutral condition that shifted to disinhibition and mirthless interpersonal detachment when pranks were salient (Study 3). Thus, at least among those most at risk for manifesting sadistic motivation, latent hostility may be overlooked amidst prank-related celebrations. This apparent implicit/overt discrepancy should be considered when designing interventions for minimizing the occurrence of sadistically motivated harm.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.045 | 0.014 |
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".