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Record W4414892162 · doi:10.7592/ejhr.2025.13.2.1043

Rage beneath the machine

2025· article· en· W4414892162 on OpenAlexaboutno aff
Christopher T. Burris, Emily Burns, Kristina Garth

Bibliographic record

VenueEuropean Journal of Humour Research · 2025
Typearticle
Languageen
FieldPsychology
TopicHumor Studies and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsAngerHostilityAggressionHappinessRage (emotion)RuminationAffect (linguistics)Sadistic personality disorderPoison control

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.903
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.118
GPT teacher head0.476
Teacher spread0.358 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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