Lessons from Bear-Walker (Mukwo-bimossae) and Water Lilies on Decolonization and Indigenization of Canadian Legal Education: An Analysis of the University of Ottawa’s Certificate in Indigenous Law
Bibliographic record
Abstract
In Anishinaabe gikinoo’amaadiwin (legal education), law – Anishinaabe inaakonigewin – is a way of life, and learning means practicing Anishinaabe inaadiziwin (Anishinaabe way of life). Whereas gikinoo’amaadiwin is a process of becoming whole, it has found little space in Canadian law schools where civil law and common law are taught exclusively. Universities, which have largely marginalized Indigenous peoples and their knowledge systems through exclusion, are beginning to address this in response to the Truth and Reconciliation Commission’s Calls to Action. This thesis reflects on how to respectfully transmit Indigenous legal knowledge within university settings. It suggests that law schools must engage in processes of decolonization and indigenization to achieve this goal. These processes involve critically identifying and challenging the colonial structures inherent in Canadian legal education and demand that the approach to teaching Indigenous legal traditions centres Indigenous ways of knowing and learning. By examining the Certificate in Indigenous Law at the University of Ottawa and my teaching experience in the program, I highlight possibilities for reimagining legal education through decolonized law programs and Indigenous legal pedagogies.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.034 | 0.027 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".