A funny thing did not happen at work: Humor styles are not strongly correlated with vocational interests
Bibliographic record
Abstract
• Examined humor styles and vocational interests. • Affiliative humor style correlated with some artistic interests. • Self-enhancing humor style correlated with adventure, nature-agriculture, and medical service. • Aggressive humor style was associated with a science interest. • Self-defeating humor style was not strongly associated with vocational interests. The role of humor in the workplace has received great attention. What is less known is how humor, specifically humor styles, correlate with vocational interests. Based on self-report responses from a community sample of 567 adults, we explored the relationships between humor styles and vocational interests. Affiliative humor style scores correlated positively with some artistic interests including performing arts and author-journalism. Self-enhancing humor style scores had moderate significant correlations with interests in adventure, nature-agriculture, and medical service. The aggressive humor style was positively associated with interests in science and had negative correlations with work styles such as accountability, planfulness, and job security. The self-defeating humor style was not strongly associated with vocational interests. When humor styles were added, with sex and age, in regression models, six of the seven vocational interest factors had significant predictions but only two models had at least one humor style significantly contributing, with the variance accounted ranging from 5% to 11%. The biology interest factor was significantly predicted by the self-enhancing humor style scores and the work style factor was significantly predicted by lower aggressive humor style scores. In general, these results suggest that humor styles are not strongly correlated with vocational interests.
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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 | 0.008 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".