Who Laughs, and Who Doesn't? Predicting Humor Skills From Personality, Social Anxiety, and Laughter Dispositions of Gelotophobia, Gelotophilia, and Katagelasticism
Bibliographic record
Abstract
Laughter-related dispositions, including gelotophobia (fear of being laughed at), gelotophilia (enjoyment of being laughed with), and katagelasticism (enjoyment of laughing at others), may explain patterns of humor use beyond broad personality traits and social anxiety. However, their incremental predictive value across distinct humor domains remains insufficiently examined. A sample of 788 Canadian university students completed self-report measures of laughter-related dispositions, HEXACO personality traits, social anxiety, and six humor domains. Hierarchical regression models assessed incremental validity beyond personality and social anxiety. Gradient boosting machine learning models were conducted to examine nonlinear effects and relative predictor importance. Gelotophilia consistently predicted greater humor use across domains. Gelotophobia predicted lower everyday humor, reduced laughing at oneself, and diminished humor under stress, even after controlling for personality and social anxiety. Katagelasticism showed minimal associations with adaptive humor domains. Machine learning analyses converged with regression findings, underscoring the robustness of these effects. Laughter-related dispositions demonstrate distinct and incremental contributions to humor use beyond personality and social anxiety. Gelotophilia and gelotophobia, in particular, represent meaningful predictors of adaptive humor engagement.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".