Humor Styles as Mediators Between Self-Evaluative Standards and Psychological Well-Being
Bibliographic record
Abstract
The authors examined how certain humor styles mediate the relations between self-evaluative standards (which form the primary evaluative component of the self-schema) and psychological well-being. As predicted, greater endorsement of positive self-evaluative standards led to the use of more affiliative humor, which, in turn, led to higher levels of social self-esteem and lower levels of depression. Also, as predicted, greater endorsement of negative self-evaluative standards led to the use of more self-defeating humor, which resulted in lower levels of social self-esteem and higher levels of depression. Further, affiliative humor also mediated the relation between negative self-evaluative standards and well-being. In this study, the greater endorsement of negative self-evaluative standards led to the use of less affiliative humor, which led to a decrease in social self-esteem. These results suggest that specific features associated with these 2 humor styles may contribute in a differential manner to an individual's level of well-being. In particular, the increased use of affiliative humor may facilitate the development and maintenance of social support networks that foster and enhance well-being. Alternatively, the greater use of self-defeating humor may result in the development of maladaptive social support networks that impede psychological well-being.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 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".