Framing Supervisory Relationships in Clinical Law: The Role of Critical Pedagogy
Bibliographic record
Abstract
Clinical work in law offers important opportunities for students to learn critical, reflective and politicized approaches to legal identity and practice. Such an approach is most meaningful when it is engaged by supervising lawyers and social workers in a clinical placement. The authors of this article, the Academic Clinic Director and Executive Director of two Windsor-based clinic programs, offer context, perspective and examples of how critical pedagogy (influenced by, but distinct from, critical legal studies) provides a roadmap for supervising lawyers and the programs in which they work. The paper briefly sets the context of the authors' teaching and practice. The authors then set out some of the interested parties in clinical legal education, including law schools, communities, students and clients. The paper concludes with ideas on how a clinical program might set out to strengthen critical pedagogy in the supervisory relationship.
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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 | 0.033 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.017 | 0.069 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.006 | 0.013 |
| 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".