Mapping social exclusion in STEM to men’s implicit bias and women’s career costs
Bibliographic record
Abstract
Why are women socially excluded in fields dominated by men? Beyond the barriers associated with any minority group's mere numerical underrepresentation, we theorized that gender stereotypes exacerbate the social exclusion of women in science, technology, engineering, and math (STEM) workplaces, with career consequences. Although widely discussed, clear evidence of these relationships remains elusive. In a sample of 1,247 STEM professionals who work in teams, we tested preregistered hypotheses that acts of gendered social exclusion are systematically associated with both men's gender stereotypes (Part 1) and negative workplace outcomes for women (Part 2). Combining social network metrics of inclusion and reaction time measures of implicit stereotypes (the tendency to "think STEM, think men"), this study provides unique empirical evidence of the chilly climate women often report experiencing in STEM. Men with stronger implicit gender stereotypes had fewer social ties to female teammates. In turn, women (but not men) with fewer incoming cross-gender social ties reported worse career fit and engagement. Moderated mediation revealed that for women (but not men), cross-gender social exclusion was linked to more negative workplace outcomes via lower social fit. Effects of social exclusion were distinct from respect. We discuss the possible benefits of fostering positive cross-gender social relationships to promote women's professional success in STEM.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".