“It's not you, it's me”: transformational leadership and self‐deprecating humor
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate leaders’ use of humor as an expression of how they value themselves relative to others. The paper suggests that humor can minimize or exacerbate the status differences between leaders and followers. The paper hypothesizes that leaders’ use of self‐ or in‐group‐deprecating humor would be positively associated with ratings of transformational leadership as they minimize those distinctions, whereas leaders’ use of aggressive humor would be negatively associated with ratings of transformational leadership because it exacerbates status distinctions. Design/methodology/approach A total of 155 undergraduates (58 males, 97 females; M age=20 years, SD =1.31) were assigned randomly to one of four conditions, each depicting a different type of humor in a leader's speech. Findings Leaders using self‐deprecating humor were rated higher on individualized consideration (a factor of transformational leadership) than those that used aggressive humor. Research limitations/implications The authors encourage future field research on the role of humor as an expression of leaders’ self‐ versus other‐orientation. Originality/value Humor and work might seem inconsistent, but this study demonstrates how leadership can use humor to improve leader‐follower relationships. Furthermore, it contributes to our understanding of self‐deprecating humor which has received scant attention relative to other forms of humor.
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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 | 0.010 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 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".