Brilliance as gender deviance: Gender-role incongruity as another barrier to women's success in academic fields
Bibliographic record
Abstract
“Brilliance,” a state of extreme intellectual ability, is stereotypically associated with men but not women. Research finds that portrayals of brilliance as a prerequisite for success contribute to women's underrepresentation in certain academic fields and high-level positions. In this work, we examined whether gender roles contribute to the perception of women as less brilliant. In four preregistered experimental studies ( N = 920), we tested whether brilliance deviates from ascribed and prescribed gender roles more for women than for men and whether such deviation places women who display their brilliance at a higher risk of experiencing backlash. In Study 1, an average intelligent and a brilliant man were rated as more similar on gender-specific traits than an average intelligent and a brilliant woman. In Study 2, while intelligence and gender individually influenced prescriptions of masculinity and femininity, their interaction did not support larger differences for female targets, indicating a lack of differential expectations by gender and intelligence. Study 3 showed that brilliant women are more likely to experience backlash at work than brilliant men, while Study 4 demonstrated that while brilliance enhances professional desirability across genders, it decreases social desirability, suggesting social costs that could affect workplace dynamics. Our results support that brilliance can be considered a form of gender-role deviance for women and might lead to a backlash. This underscores the need for policies to counteract gendered stereotypes of brilliance, which hinder women's career advancement and contribute to the gender gap in 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 | 0.013 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".