Leaning In and Disrupting It: How Women of Color Lead
Bibliographic record
Abstract
This symposium explores how women of color navigate and disrupt leadership stereotypes across diverse political, organizational, and educational contexts. The five empirical studies examine intersectional biases that affect leadership perceptions and outcomes for Black, South-Asian, and East-Asian women. Using diverse methodologies like content analysis, survey-based mediational analysis, and narrative analysis, these studies draw from samples in the U.S., Canada, and the UK. Together, they challenge traditional leadership paradigms, propose inclusive frameworks, and offer actionable strategies for fostering equity. This session contributes to leadership and diversity scholarship, addressing key themes of equity and inclusion for the Diversity, Equity, and Inclusion (DEI), Managerial Cognition (MOC), and Careers (CAR) divisions. The experiences of women of color in leadership roles are shaped by the intersection of racial and gender biases, which create unique barriers while simultaneously fostering distinctive leadership styles. Black women often navigate the dual burden of being viewed through the lens of the
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".