Heroes, Tricksters, Monsters, and Caretakers: Indigenous Law and Legal Education
Bibliographic record
Abstract
Teaching Indigenous peoples’ own law in Canadian law schools presents significant challenges and opportunities. Materials can be organized in conventional or innovative ways. This article explores how law professors and others might best teach Indigenous peoples’ law. Questions canvassed include: whether Indigenous peoples’ law should primarily be taught in Indigenous communities, whether such law should even be taught in law schools, whether it is possible to categorize Indigenous peoples’ law or teach it in English, and whether it is possible to theorize Indigenous peoples’ law within a single framework or organize the subject within common law categories. While this article suggests that Indigenous peoples’ law can be discussed in numerous ways, including within conventional law school frameworks, it emphasizes that such law is best taught in other ways. Indigenous legal traditions should be organized in accordance with Indigenous frameworks. Some of these frameworks include Heroes, Tricksters, Monsters, and Caretakers. Using these Anishinaabe law examples, this article stresses how the teaching of Indigenous peoples’ law should be done in culturally appropriate ways that open rather than confine fields of inquiry within Indigenous law and practice.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.017 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".