Humor Styles and the Intolerance of Uncertainty Model of Generalized Anxiety
Bibliographic record
Abstract
Past research suggests that sense of humor may play a role in anxiety. The present study builds upon this work by exploring how individual differences in various humor styles, such as affiliative, self-enhancing, and self-defeating humor, may fit within a contemporary research model of anxiety. In this model, intolerance of uncertainty is a fundamental personality characteristic that heightens excessive worry, thus increasing anxiety. We further propose that greater intolerance of uncertainty may also suppress the use of adaptive humor (affiliate and self-enhancing), and foster the increased use of maladaptive self-defeating humor. Initial correlational analyses provide empirical support for these proposals. In addition, we found that excessive worry and affiliative humor both served as significant mediators. In particular, heightened intolerance of uncertainty lead to both excessive worry and a reduction in affiliative humor use, which, in turn, increased anxiety. We also explored potential humor mediating effects for each of the individual worry content domains in this model. These analyses confirmed the importance of affiliative humor as a mediator for worry pertaining to a wide range of content domains (e.g., relationships, lack of confidence, the future and work). These findings were then discussed in terms of a combined model that considers how humor styles may impact the social sharing of positive and negative emotions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".