Gender Stereotypes about Leadership and Entrepreneurship: Taking Stock and Looking Ahead
Bibliographic record
Abstract
Countries around the world have shared understanding and unconscious biases of what abilities and personality characteristics men and women possess, and how each gender should or should not behave. Gender stereotypes tend to cast men as more competent, strong, and agentic, and women as more communal, warm, and other-oriented compared to members of the opposite sex. Partly because of such gender stereotypes, men and women are perceived and evaluated differently, and they perceive and evaluate themselves differently. Gender stereotypes are consequential: they impact women’s career choices and advancement to the management and top-level positions in the corporate sector, their likelihood of founding new ventures and receiving key financial, human capital, and network resources necessary for scaling up their businesses, their job performance, cognitive resources, and professional fulfillment. Our symposium makes a direct contribution to the 2022 AOM’s theme “Creating a Better World Together”, promoting cross-disciplinary dialogue among scholars from (social) psychology, sociology, entrepreneurship, economics, and organization theory to deepen our understand of ‘how, why, and when’ gender stereotypes are likely to influence male-female differentials in the corporate leadership and entrepreneurship. Our symposium has two goals. We bring together scholars to take stock of the literature, identify puzzles in extant work and future research directions, and build bridges for interdisciplinary research. Our symposium features pioneers from (social) psychology whose work on stereotypes has been path-breaking and is considered foundational in research on attitudes, gender, and diversity, along with strong emerging scholars working at the ‘biases and entrepreneurship’ research nexus. Our second goal is to discuss the effectiveness of existing behavioral interventions to combat gender stereotypes and increase gender leadership diversity in both labor market contexts. The symposium is designed to foster a dialogue between the panelists, discussants, and the audience centered around five discussion topics. It will close with the organizers’ summary synthesizing the scholarly exchange. The symposium is designed for researchers in ENT, OMT, and GDO but could be of interest to a much broader AoM audience interested in research on stereotypes, diversity in leadership and innovation, and organizational performance.
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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 | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".