Navigating Administrative Leadership: An Examination of Brown Women Educators’ Leadership Experiences in K-12 Public Schools in Ontario
Bibliographic record
Abstract
This qualitative research grounded in notions of Seva Service Leadership examines the experiences of Brown women in formal (administrative) leadership roles in K-12 Public Schools in Ontario. Through semi-structured interviews, 15 self-identifying Brown women educators (BWEs) shared how they navigate their leadership journeys, factors that influence their decisions to enter formal leadership roles, and what sustains them in their administrative leadership roles. The term ‘Brown’ is used in this study to refer to women who self-identify as Indian, Pakistani, Bangladeshi or Sri Lankan descent/heritage and/or belonging to the South Asian diaspora. BWEs navigating leadership roles in K-12 Ontario public school boards face individual and systemic barriers, while engaging in strategies that sustain their leadership journeys. The findings from the participants’ experiences revealed: a) the Need for and Importance of Mentoring, b) the Need to Prove Oneself, c) Importance of Representation, d) Experiencing and Navigating Microaggressions and Whiteness, e) Navigating Internal Tensions f) Survival Strategies, and g) Pay it Forward. Findings also revealed that BWEs interpret their experiences by using Seva Service Leadership actions and subsequently their agency. Finally, this research can inform hiring and recruitment practices of not only Brown women in formal leadership roles in K-12 schools in Ontario, but all diverse populations. The study concludes with implications for practice and future research.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.013 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".