Examining the Intersectionality of Gender and Race in Leadership Experiences Within U.S. Higher Education: Towards Equitable Representation and Social Justice
Bibliographic record
Abstract
In the ever-evolving landscape of U.S. higher education, representation in leadership roles remains a prominent concern, particularly when viewing through the lenses of gender and race. This study critically examines the intersectionality of gender and race in leadership roles, delving deep into these two critical social identities and their implications on leadership positions in higher education in the United States. The study adopts a literature review and rigorous content analysis despite some researchers tiptoeing around the topic and looking at racial or gender disparities in isolation. The research uncovers systemic barriers perpetuating disparities in leadership roles. The findings not only reveal the unique challenges individuals face at this intersection but also shed light on potential strategies to champion equitable representation. By bridging the existing knowledge gap, this study underscores the importance of a holistic understanding of educational equity and diversity, further advocating for progressive reforms in leadership within U.S. higher education.
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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 | 0.001 |
| 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 | 0.001 |
| 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".