Dark Humour: The Associations Between Humour Style, Psychopathic Traits, and Empathy
Bibliographic record
Abstract
This study investigates the relationship between styles of humour, facets of psychopathy, and three facets of empathy, aiming to discern whether these variables are interrelated and how. A sample of 884 undergraduate students completed a series of questionnaires online and results were analyzed via structural equation modelling and correlations. Key findings reveal a strong positive relationship between aggressive humour, psychopathic traits, and lack of motivational empathy and a strong positive relationship between affiliative humour, affective empathy, and motivational empathy. These findings reveal that empathy and humour are largely intertwined, and therefore humour styles consistent with a lack of empathy may be an important feature of psychopathy in non-clinical settings. For instance, negative humour may be utilized by non-criminal psychopaths to harm others without facing legal repercussions. This research contributes to the study of psychopathy and bridges the gaps in research by investigating the presentation of psychopathic traits outside of institutional environments.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".