The Role of the School Leader in Indigenous Education in Publicly Funded Ontario Schools
Bibliographic record
Abstract
This study examined the ways in which school leaders are engaging in the work of Indigenous education within publicly funded schools in the province of Ontario. Interviews with ten school leaders were conducted to determine the skills, knowledge, strategies and perspectives they brought to their work and the openings and barriers they encountered at various levels of the system, in their attempts to move forward. A literature review provided an understanding of the existing research conducted in Ontario related to student and staff knowledge, curriculum and policy, and transformational leadership, as well as bringing Indigenous voice to the forefront in terms of the scope that Indigenous education and pedagogy can include. Critical race theory offered a theoretical lens through which to interpret the data, as this theory purports that colonization has resulted in vast inequities and the perpetuation of oppressive structures that benefit White supremist systems at the expense of Indigenous peoples, and peoples of Colour more generally. Operating within that system, the research highlighted the ways in which principals and vice principals work within their direct sphere of influence (the school) to support the learning of staff and students related to Indigenous education. A nested conceptual framework provided clarity to examine the impacts and influences of the various levels (society, Ministry of Education, School Board, community, Indigenous community, and school) impacting the work of school leaders. The findings also demonstrated systemic gaps and areas of need for further attention and funding. While pockets of in-depth work and professional development in Indigenous education were evident, foundational gaps in student and staff knowledge were also noted, along with questions about cultural appropriation and resistance to change. School leaders pointed out the importance of ongoing Indigenous partnerships within schools, and the need for further funding and structures to support such relationships. To address existing gaps, recommendations included curriculum overhaul through authentic partnerships with Indigenous Elders and Knowledge Keepers, Indigenous educators, and government funding to make such change a reality.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.029 | 0.014 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 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".