An Inside Job: Engaging with Indigenous Legal Traditions through Stories
Bibliographic record
Abstract
There has been a growing momentum toward a greater recognition and explicit use of Indigenous laws in the past several years. According to the Truth and Reconciliation Commission’s final report, the revitalization and recognition of Indigenous laws are essential to reconciliation in Canada. How, then, do we go about doing this? In this article, we introduce one method, which we believe has great potential for working respectfully and productively with Indigenous laws today. We engage with Indigenous legal traditions by carefully and consciously applying adapted common law tools, such as legal analysis and synthesis, to existing and often publicly available Indigenous resources: stories, narratives, and oral histories. By bringing common pedagogical approaches from many Indigenous legal traditions together with standard common law legal education, we hope to help people learn Indigenous laws from an internal point of view. We share experiences that reveal that this method holds great potential as a pedagogical bridge “into” respectful engagement with Indigenous laws and legal thought, within and across Indigenous, academic, and professional communities. In conclusion, we argue that, while this method is a useful tool, it is not intended to supplant existing learning and teaching methods, but rather to supplement them. In practice, we have seen that this method can be complementary to learning deeply through other means. There are many methods to engage with Indigenous laws, and there needs to be critical reflection and conversations about them all.
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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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.029 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".