Understanding the effects of (dis)similarity in affiliative and aggressive humor styles between supervisor and subordinate on LMX and energy
Bibliographic record
Abstract
Abstract Prior studies on humor have primarily focused on the effects of either leader or subordinate humor styles and thus have neglected the influence of (dis)similarity in humor styles between supervisor and subordinate. We draw on the similarity-attraction perspective to suggest that (dis)similarity in supervisor’s and subordinate’s affiliative and aggressive humor influences workplace energy via the leader-member exchange (LMX). Results show that LMX is higher when leader and subordinate both display high-affiliative and low-aggressive humor behaviors. Furthermore, LMX is higher when a low-affiliative humor subordinate is paired with a high-affiliative humor leader and when a high-aggressive humor subordinate is paired with a low-aggressive humor leader. Our findings reveal that LMX mediated the relationship between (dis)similarity in humor styles and employee energy. Taken together, our results contribute to the understanding of the effects of similarity and dissimilarity in humor behaviors in energic relational processes.
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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 | 0.001 | 0.001 |
| 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".