Towards Implementing the Truth and Reconciliation Commission's Calls to Action in Law Schools: A Settler Harm Reduction Approach to Racial Stereotyping and Prejudice Against Indigenous Peoples and Indigenous Legal Orders in Canadian Legal Education
Bibliographic record
Abstract
Many Canadian law schools are in the process of implementing the Truth and Reconciliation Commission’s Call to Actions #28 and #50. Promising initiatives include mandatory courses, Indigenous cultural competency, and Indigenous law intensives. However, processes of social categorization and racialization subordinate Indigenous peoples and their legal orders in Canadian legal education. These processes present a barrier to the implementation of the Calls. To ethically and respectfully implement these Calls, faculty and administration must reduce racial stereotyping and prejudice against Indigenous peoples and Indigenous legal orders in legal education. I propose that social psychology on racial prejudice and stereotyping may offer nonIndigenous faculty and administration a familiar framework to reduce the harm caused by settler beliefs, attitudes, and behaviors to Indigenous students, professors, and staff, and to Indigenous legal orders. Although social psychology may offer a starting point for settler harm reduction, its application must remain critically oriented towards decolonization.
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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 | 0.036 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.057 | 0.035 |
| Scholarly communication | 0.020 | 0.007 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.011 | 0.016 |
| 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".