The Social-Cognitive Functions of Workplace Humor, Gender, on Promotability
Bibliographic record
Abstract
Humor is a pervasive social phenomenon in the workplace context as more and more organizations nowadays are pursuing a fun work environment. While previous literature had examined the evaluations of leader humor, trait humor of employees in the workplace context had not been examined with career advancement prospects, particularly promotability. Our study investigated the effects of both positive and negative traits of workplace humor reported by employees on their promotability evaluated by supervisors using a dyadic work sample in Israel (N=142 dyads) with a cross-sectional design. Interactions between positive/negative workplace humor, employee gender, and supervisors’ gender were examined. The results suggested that employee trait humor is a critical predictor for promotability when gender is incorporated as a moderator, even after controlling for Big Five personality traits and emotional intelligence. In particular, humorous male subordinates are more likely to be perceived as promotable especially if their supervisors are male while humorous female subordinates are less promotable. Moreover, male and female supervisors evaluate promotability associated with workplace humor differently. Our study replicated and extended the earlier studies concerning the gender bias of humor in the workplace from a subordinate perspective, and yielded important implications for leadership emergence and diversity, equity, and inclusion.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".