Challenges in Gendering Indigenous Legal Education: Insights from Professors Teaching about Indigenous Laws
Bibliographic record
Abstract
Abstract In the past decade there has been a distinct increase in literature on Indigenous laws. Calls to teach about Indigenous laws in postsecondary institutions in Canada have also intensified. This growth and these calls are significant, yet as with all fields of inquiry and teaching, there are also gaps. Gender continues to be under-addressed in work on Indigenous legal education. Drawing on interviews with twenty-three professors who teach about Indigenous law at postsecondary institutions in Canada, I examine the challenges in gendering Indigenous legal education. The professors all expressed that it is important to engage with gender when teaching, but the majority were experiencing significant challenges in actually doing so in practice. It is essential to understand how these challenges are entangled with gendered power dynamics and broader structural barriers, as they will continue to limit Indigenous legal education if not directly deconstructed and changed. Overall, the interviews signal the need for increased institutional support and change, more educational resources, eliminating discrimination, and ongoing discussion about gender and Indigenous law.
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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 | 0.019 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.041 | 0.032 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".