Sexism in teams: Exposure to sexist comments increases emotional synchrony but eliminates its benefits for team performance
Bibliographic record
Abstract
In a world where teams serve as the backbone of collaboration and innovation, women must feel safe when contributing to teamwork. Unfortunately, an increasing number of women report experiencing sexual harassment in workplaces and other collective settings. This research included an examination of how exposure to sexist insults affects collaborative efforts. We argue that in addition to its well-documented effects on individuals, sexism within teams undermines team performance. Hence, emotional synchrony-temporally coordinated emotional facial expressions between individuals-loses its ability to enhance collaboration when team members are exposed to sexism. Under the threat of sexist comments, emotional synchrony signals social bonding rather than focusing team members on performance goals. To test this theory, 177 woman dyads interacting on a video-conferencing platform received instructions for a cooperative task with/without sexist comments from an actor-experimenter in sexism/control conditions. Emotional synchrony was assessed through temporal alignment in facial expressions between dyad members and was correlated with team performance. Our findings revealed a significant increase in facial expressive synchrony among teams in the sexism condition. Whereas facial expressive synchrony predicted better performance in the control condition, its classic positive effects on team performance vanished under sexism. These results suggest that exposure to sexism, while enhancing social cohesion, eliminates the benefits of emotional synchrony, which is considered the social glue of collective action. These findings suggest that zero-tolerance policies for sexual harassment are not only more ethical but can also promote effective teamwork.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".