The E-Team Project: A Teamwork Approach to Clinical Legal Education
Bibliographic record
Abstract
In this article the author argues that the University of Toronto’s Emergency Team (E-Team)—a student pilot project created to assist people facing deportation on short notice—provided a critical service to its clients and gave its student members a unique opportunity to learn real-world legal skills. The first part of this article reviews the project’s outcomes and concludes that it was a success: the E-Team won nine of its ten cases, and its members credit the project both with teaching them crucial legal competencies that they did not encounter elsewhere and with fostering their passion for social justice law. The second and third parts analyze the E-Team’s structure, identifying the organizational principles that allowed the students to work well together and arguing that teamwork, in a context marked by urgency and high stakes, offers exceptional pedagogical opportunities. In the final section, the author suggests that while the refugee law context is unusual in many respects, the E-Team model could nonetheless be applied in other areas of law practiced in Ontario’s student legal clinics, and in particular, could be adapted to be useful in less urgent situations where the stakes are lower.
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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 | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".