Mino-bimaadiziwin: ReIndigenizing through Land-based Learning
Bibliographic record
Abstract
In response to the Truth and Reconciliation’s 62nd and 63rd Calls to Action, the author takes a servant leadership approach to embedding Indigenous knowledge to the K-12 classrooms in Ontario with a focus on reIndigenizing through land-based learning. Student well-being and achievement data show Indigenous students in both provincial and Indigenous community schools are below that of their non-Indigenous peers, and the impact of residential schools continues in Treaty 3 territory is an intergenerational crisis that demands immediate attention and shift for educational leadership. As a Métis scholarly practitioner, the author centres Indigenous research and personal positionality in creating a change implementation plan which focuses on learning from, on, and with the land as a daily act of reconciliation. The traditional medicine wheel is used throughout the Dissertation-in-Practice to align holistic, lifelong learning with change leadership, monitoring, evaluation, and disrupting the status quo. Culturally responsive pedagogy is explored through research and practical examples of shifting practice, policy, and ontological perspectives to outline practical solutions for complex issues. The work is centred on mino-bimaadiizin, the Anishinaabek teaching of leading a good life.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".