Has the Concrete Ceiling Cracked Yet? A Systematic Review of the Barriers Faced by Minority Leaders in Higher Education
Bibliographic record
Abstract
A growing number of higher education (HEs) are focusing on diversity initiatives in the 21st century. However, rhetoric does not always correspond to reality. For example, diversity is still not reflected well in academic leadership (Williams, 2013). HEI organizational structures continue to reflect a society where most power and resources are held by White men (O’Connor, 2017). In response to minority groups’ marginalization, HEs have initiated diversity initiatives, but these initiatives often result in well-written mission statements and superficial improvements (Henry et al. 2016). According to Stanley (2006), diversity and inclusion are not aligned with their application in Canadian HEs. In HEs led predominantly by White men, where minority leadership is underrepresented, understanding the barriers minority Faculty face in advancing to leadership roles can provide insight into how their identities have shaped their opportunities. This study examines recent research on minority leadership in HEs conducted between 2017 and 2022. It sheds light on critical issues facing HEs today as well as the enablers to overcoming these barriers.
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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 | 0.027 | 0.086 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".