The Conceptualization, Measurement, and Role of Humor as a Character Strength in Positive Psychology
Bibliographic record
Abstract
In positive psychology, humor has been identified as one of 24 character strengths considered ubiquitously important for human flourishing. Unlike the other strengths, humor was a late addition to this classification system and its status as a strength continues to be somewhat controversial. Therefore, the first purpose of this study was to explore the associations between humor and several outcome variables of relevance to positive psychology (happiness, routes to happiness, resilience, and morality). The second purpose was to explore how best to conceptualize and measure humor as a character strength by comparing the Values in Action Inventory of Strengths (VIA-IS) Humor Scale with the Humor Styles Questionnaire (HSQ) in their ability to predict the outcome variables. A sample of 176 participants completed questionnaires assessing the humor and positive psychology constructs. The results indicated that the humor measures significantly predicted most of the outcome variables, supporting the importance of humor in positive psychology. Furthermore, although the VIA-IS Humor scale and positive humor styles on the HSQ showed considerable overlap, the negative humor styles added significantly to the prediction of outcome variables beyond these positive humor measures, supporting the importance of assessing maladaptive as well as adaptive uses of humor in research on positive psychology. These findings suggest that the HSQ may be a more useful measure than the VIA-IS Humor scale in future research in this field.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".