Professors’ “feminine” behavioral cues in the classroom close gender gaps in participation.
Bibliographic record
Abstract
Record numbers of women students have enrolled in graduate business programs-a path to greater career prospects. Yet gender disparities in grades and career outcomes persist. Research suggests that one reason why they persist is because business schools are-and have been historically-male-dominated with cultures characterized by masculine defaults. These cultures regard stereotypically masculine characteristics and behaviors (e.g., being assertive and competitive) as neutral, necessary, or standard. In business school settings, we investigate whether there are gender disparities in a critical aspect of students' experiences: classroom participation. In an observational field study of 3,159 students across 76 Master of Business Administration classrooms, we find a gender gap in actual student participation behavior. Importantly, however, our findings indicate that this gap is not inevitable. Using thin-slice impressions of professors' behaviors during classroom interactions, we find that when professors diverge from the masculine default by exhibiting "feminine" behavioral cues (e.g., subtle other-oriented behaviors), the gender participation gap closes. In a second, preregistered experiment, we provide causal evidence that professors' behavioral cues affect student participants' anticipated participation by shaping their experiences of inclusion. Our findings highlight that, in male-dominated settings, shifting professors' behavioral cues away from masculine defaults has the potential to create more equitable spaces. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
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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 | 0.001 | 0.008 |
| 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 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".