Femininity Is Favorable: Sexually Dimorphic Facial Features Affect Assessments of White Women's Leadership Abilities
Bibliographic record
Abstract
Women remain underrepresented in leadership positions traditionally held by men. Research on role congruity and backlash has shown that aspiring women versus men leaders are more negatively evaluated when they enact agentic behaviors. We examined whether sexually dimorphic facial features, which are associated with agentic and communal trait impressions, constitute a nonverbal barrier to White women's leadership in a college setting. Manipulated masculinized versus feminized facial features elicited, respectively, higher dominance and lower warmth impressions. Aspiring women leaders with masculinized versus feminized facial features received less favorable evaluations for several leadership roles, whereas men's evaluations were unaffected by varying features. Contrasting past work, aspiring women leaders were overall more favorably evaluated. This difference related to beliefs that college-aged White women versus men are more competent, responsible, and warm. These findings provide novel evidence that feminized facial features offer a unique advantage to aspiring college-aged White women leaders.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".