Touring Turtle Island: Fostering Leadership Capacity to Support First Nations, Métis, and Inuit Learners
Bibliographic record
Abstract
This paper reports on findings from a research study that examined the design, delivery, and effects of a graduate level summer institute, the aim of which was to foster the capacity of educational leaders to support First Nations, Métis, and Inuit (FNMI) learners. Our study is conceptually framed using elements of critical race and Whiteness theory, and red pedagogy/culturally relevant pedagogy. We designed the institute and our methods around Kirkness's and Barnhardt’s (1991) 4 R’s of success in higher education environments: relevance, reciprocity, responsibility, and respect. Data for the study were gathered using a qualitative, inquiry-based methodology, and articulated using Indigenous storywork and story. The primary data sources included online surveys and sharing circles conducted with past students of the course. Findings suggest that the summer institute helped to disrupt colonial assumptions; increase respect for Aboriginal knowledges, values, and experiences; offer relevance to educators who were able to use the learnings of the course in their own professional contexts; affirm the need for reciprocity between schools and FNMI families; and develop educational leaders’ sense of responsibility to ensure FNMI learners are supported in the public school system. Keywords: Indigenous education; educational leadership; educational administration; critical race theory; Whiteness theory; red pedagogy; culturally relevant pedagogy; teacher education
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".