Individual differences in the way observers perceive humour styles
Bibliographic record
Abstract
Humour has been conceptualized as styles, which vary based on their function (Martin, Puhlik-Doris, Larsen, Gray, and Weir, 2003). Research examining if and how observers perceive this intent is limited. The current study addresses this research gap by examining the perceptions of Martin et al.’s (2003) four humour styles. Additionally and of particular interest, was whether self-defeating humour and another self-directed humour style, self-deprecating humour, were perceived as two independent humour styles. Despite being similar in content, self-deprecating humour is associated with higher self-esteem and self-defeating humour with lower self-esteem. Two hundred and four students watched comedy clips and completed a survey online. Participants were asked to categorize each video clip by humour style and to rate the self-esteem of the target (i.e. comedian). Results revealed that humour styles are distinguishable by observers with participants predominantly selecting one humour style over the others for each clip. In support of the second hypothesis, targets who were categorised as using self-deprecating humour were perceived as having higher self-esteem than those categorised as using self-defeating humour, illustrating a distinction in the perception of these humour styles at an interpersonal level.
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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 | 0.010 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".