Gender-Inclusive Norms Release Pressure for Masculine-Typed Dominance to Lead in STEM
Bibliographic record
Abstract
Women remain underrepresented in science, technology, engineering, and math (STEM), which curtails economic growth. Three preregistered experiments with STEM employees and one correlational field study with engineering students examined how an inclusive culture, with its weaker masculine defaults, frees women (and men) from the need to conform to dominant forms of leadership. Unlike men, women who engage in stereotypically masculine dominance encounter backlash for their norm-violating behavior. We further tested whether more inclusive cultures reduce women’s gap between their desired career advancement and the advancement they think will be possible, without harming men’s aspirations. In the three experiments (N=376, N=744, N=417), we randomly assigned STEM employees to imagine working as a leader in an organization with either a competitive-meritocratic (CM) or equity-inclusive (EDI) culture. Confirming our assumptions, results showed that women—and men—used less dominance in the EDI than CM culture. The EDI culture further released women’s otherwise constrained career aspirations without adding constraints for men. Mediation showed that both dominance and career constraint were reduced because of weaker perceived masculine defaults. The field study (N=265) examined leadership behavior in a natural setting with engineering university students working together in teams where leadership emerged organically. Results showed that both women and men used less dominant leadership behaviors in more egalitarian (rather than hierarchical) teams. Moreover, in more egalitarian teams, women received higher grades than men, whereas no such difference existed in more hierarchical teams. In concert, gender-inclusive cultures benefit everyone, and particularly women, who are underrepresented in STEM.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".