Beyond humor styles: the nature of humor types and differences in basic personality traits from Zuckerman’s Alternative Five-Factor Model
Bibliographic record
Abstract
BACKGROUND: Findings show that the complex nature of humor and its personality basis can be more comprehensively understood if humor styles are analyzed simultaneously within humor types, rather than separately. PARTICIPANTS AND PROCEDURE: = 353) of self-report responses to the Humor Styles Questionnaire (HSQ) and the Zuckerman-Kuhlman-Aluja Personality Questionnaire-Short Form, this paper outlines how the HSQ responses result in three humor use types following cluster analysis. Cluster differences in humor styles and personality traits were analyzed using ANOVA. RESULTS: In both samples, a humor type characteristic of individuals who scored lower in the positive and higher in the negative humor styles was revealed. People within this humor type also scored significantly higher in the personality measures of neuroticism and aggressiveness. A second humor type replicated in the two studies described individuals scoring higher for each of the four humor styles. People within this type also scored significantly higher on extraversion and sensation seeking, suggesting a need for cortical arousal. The third humor type members scored lower in each of the humor styles (apart from the affiliative humor style scores for one of the samples). This humor type requires further investigation. CONCLUSIONS: In general, humor types provide an additional understanding of humor use as people within the types differ for specific personality dimensions.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".