Those Who Teach Must Also Do: Diversity, Equity and Inclusion in Legal Education and the Canadian Armed Forces
Bibliographic record
Abstract
Diversity, Equity and Inclusion (DE&I) initiatives have become a priority for many organizations within Canada. In legal academia it has become both a procedural and substantive imperative, as it grapples with meaningful integration of these considerations, and appropriate adaptation to current social and technological challenges. This paper sketches selected considerations in implementing DE&I within legal education, and transplants them into Canadian Armed Forces (CAF) engagements with DE&I implementation, with a focus on the transmission of legal norms and values in a non-legal environment and teaching context, using an explicitly socio-legal orientation. Drawing from legal education literature highlighting the challenges and opportunities within the university, and key insights regarding DE&I implementation’s history and current developments within the CAF derived by scholars in a themed-2020 conference, I argue that a process of translation and adaptation of legal education practices and engagement with DE&I into the CAF context will provide valuable insights into both communities of practice and transform and be transformed in the process, in particular with developing key concepts, solidifying abstract concepts and challenges, leveraging case study and simulation techniques, exploiting remote and hybrid pedagogical tools, and furthering legal education engagement outside the academy.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.049 | 0.038 |
| Scholarly communication | 0.016 | 0.005 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".