Learning, Teaching & Practising Systemic Advocacy in Legal Clinics: A Conversation
Bibliographic record
Abstract
Clinical programs have incorporated systemic advocacy in various ways for decades; indeed, for many clinics systemic advocacy is a philosophical and practical imperative. For those legal clinics with students working, taking credit, or volunteering, incorporating meaningful systemic advocacy programming brings with it a host of challenges. This article, framed as a conversation between two women involved with clinical legal education in Windsor, Ontario and Saskatoon, Saskatchewan, was born out of the practical frustrations and joys of this work. The article illuminates the theoretical, pedagogical, and administrative challenges of meaningfully incorporating students into the day-to-day realities of systemic advocacy. Although the authors are careful not to make prescriptions or speak for other clinics, the article proposes potential models to incorporate community-based systemic advocacy in student clinical legal education programs.
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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 | 0.044 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.046 | 0.071 |
| Scholarly communication | 0.026 | 0.020 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.017 | 0.029 |
| Insufficient payload (model declined to judge) | 0.005 | 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".