Bibliographic record
Abstract
What happens when you express anger at work? A large body of work suggests that workers who express anger are judged to be competent and high status, and as a result are rewarded with more status, power, and money. We revisit these claims in four pre-registered, well-powered experiments (N = 3,852), conducted in the US, using the same methods used in previous work. Our findings consistently run counter to the current consensus regarding anger's positive role in obtaining status and power in the workplace. We find that when men and women workers express anger they are sometimes viewed as powerful but they are consistently viewed as less competent. Importantly, we find that angry workers are penalized with lower status compared to workers expressing sadness or no emotions. We explore the reasons for these findings both experimentally and descriptively and find that anger connotes less competence and warmth and that anger expressions at work are perceived as inappropriate, an overreaction, and as a lack of self-control. Moreover, we find that people hold negative attitudes toward workplace anger expressions, citing them as relatively more harmful, foolish, and worthless compared to other emotional expressions. When we further explore beliefs about what can be accomplished by expressing anger at work, we find that promoting one's status isn't one of them. We discuss the theoretical and applied implications of these findings and point to new directions in the study of anger, power, and the workplace.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".