Beyond Reconciliation: Decolonizing Clinical Legal Education
Bibliographic record
Abstract
How can legal clinics and clinical legal educators respond to the ongoing harms of settler colonialism? At a time when "reconciliation" is top of mind for many legal educators in light of the Truth and Reconciliation Commission calls to action, can reconciliation be taken up in a meaningful way through clinical legal education? Does reconciliation demand decolonization and if so can clinical legal education work towards decolonization? These are the questions we consider in this article. Drawing on our respective experiences with the University of British Columbia and the University of Saskatchewan’s clinical law programs, and grounding our analysis in the critical literature on settler colonialism and decolonization, we propose that the aim of reconciliation, at least as it is typically understood, is not enough and that we must go further to challenge the structure of settler colonialism by decolonizing and Indigenizing clinical legal education. We argue that decolonial approaches and engagement with processes of Indigenization in both the academic and practical aspects of clinical law programs can intervene in normative legal education and challenge the colonial hegemony underpinning the Canadian legal system. Ultimately, we propose that it is unsettling, productive and essential for those of us involved with clinical legal education in Canada to learn with and from Indigenous communities about the challenges and possibilities of working towards decolonial justice.
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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 | 0.032 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.031 | 0.086 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.003 | 0.029 |
| Research integrity | 0.007 | 0.016 |
| 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".