Benevolent and Corrective Humor, Life Satisfaction, and Broad Humor Dimensions: Extending the Nomological Network of the BenCor Across 25 Countries
Bibliographic record
Abstract
Benevolent and corrective humor are two comic styles that have been related to virtue, morality, and character strengths. A previous study also supported the viability of measuring these two styles with the BenCor in 22 countries. The present study extends the previous one by including further countries (a total of 25 countries in 29 samples with N = 7813), by testing the revised BenCor (BenCor-R), and by adding two criterion measures to assess life satisfaction and four broad humor dimensions (social fun/entertaining humor, mockery, humor ineptness, and cognitive/reflective humor). As expected, the BenCor-R showed mostly promising psychometric properties (internal consistency and factorial validity). Consistent with previous studies, benevolent humor correlated positively with life satisfaction in most countries, while corrective humor was uncorrelated with life satisfaction. These relationships were only slightly changed when controlling for social fun/entertaining humor and mockery, respectively. Benevolent humor was mostly positively associated with cognitive/reflective humor, followed by social fun/entertaining humor and mockery. Corrective humor was mostly positively associated with mockery, followed by cognitive/reflective and social fun/entertaining humor, although these relationships differed between the countries. Overall, the present study supports the viability of benevolent and corrective humor, which has yet received insufficient attention in psychology, for cross-cultural investigations and applications of humor, well-being, and morality.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".