Bibliographic record
Abstract
AbstractThis qualitative research study answered the following question: What are some strategies and considerations that Black superintendent leaders use to navigate the Ontario education system? I investigated how Black superintendents define their own strengths, behaviours, and attitudes, as well as the strategies they used to navigate the educational system despite the barriers presented by racism and oppression. I draw on historical and current studies on Black leadership and interviews to provide a deeper understanding of the multifaceted work of Black superintendents as they navigate the Ontario education system. I interviewed nine Black superintendents with various experiences from district school boards in Ontario and highlighted common themes, leadership experiences, challenges, and opportunities. I learned from their experiences and identities, as well as where they live, work, and learn. They shared the strategies that they use to navigate the organization, their positionality, their relationships, and their job and assigned duties. Historical data has shown that where and how Black leaders live, eat, walk, and work has always been about navigating oppressive systems that create barriers for some while privileging others. Findings indicated that all the Black superintendents used strategies such as networking, mentorship, family, preparation, and community supports to navigate relationships, the education system, positionality, and their assigned jobs or roles. All Black superintendents talked about Black excellence and leaving a legacy for all students to succeed. They also talked about Black fatigue and the emotional toll that leading while being Black took on them. They also spoke with pride about how networking and sharing the strengths and opportunities in their community and affinity groups supported their leading and learning.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.028 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".