Supervision in the Clinic Setting: what we Really Want Students to Learn
Bibliographic record
Abstract
This paper focuses on certain key elements of student supervision in Community Legal Services at Western University in London, Canada. Our clinic offers a very broad range of legal services, ranging from criminal law to wills, and consumer law to housing, with 125-150 students taking part in 800-1,000 files per year.The first part of this paper will examine compliance with the supervision requirements of the profession’s governing body. Clinic supervision in a clinic must start with compliance with the regulator. The supervision requirements of the Law Society of Ontario are set out to demonstrate the standards Community Legal Services must meet.This paper will then discuss the classroom component consisting of lectures and simulation exercises where we deal with professional identity, ethical issues, sensitization to the lives of our clients, awareness of the importance of access to justice, and the capacity of legal processes.I will discuss our online materials for the classroom, including our Caseworker Manual which provides guidance in substantive law, court/tribunal rules, and clinic policies and procedures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".