Leaders laughing in the line of fire: An emotional aperture perspective on leader laughter in response to critical questions.
Bibliographic record
Abstract
Leaders are frequently put in the difficult position of repudiating critical questions in front of their followers. To help manage this situation, leaders sometimes express laughter in the hopes that it will "lubricate" their interaction and reduce perceptions that they are aggressive or confrontational with the critical questioner. Ironically, leaders' laughter may backfire by diminishing their apparent friendliness and approachability in the eyes of the witnessing followers. In this article, we employ an emotional aperture perspective to examine two seemingly contradictory theoretical perspectives regarding the potential impact of laughter on the witnessing followers' perception of a leader's warmth and effectiveness. Findings from nine studies across 2,012 adults show that leader laughter-even expressed briefly-bolsters or damages leader effectiveness depending on one important contingency: whether the leader's laughter is shared by the questioner. Unshared laughter reduces leader effectiveness by undermining leaders' apparent warmth, while shared laughter increases leader effectiveness by enhancing leaders' apparent warmth. We discuss implications for the literature on emotion expression, leadership events, and leader perception and influence. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".