Clinical and Experiential Learning in Canadian Law Schools: Current Perspectives
Bibliographic record
Abstract
What are some of the challenges and possibilities animating modern Canadian clinical and experiential learning in law? This question was the starting point for our research, which examined two sets of data. In the first part of this project, we analyzed available information on existing clinical and experiential learning programs in Canadian law schools. This data revealed a growing quantity and variety of programs across the country. We then held qualitative interviews with deans, professors, and clinicians across Canada regarding their views of clinical and experiential learning. While the interviews suggested that many of the same financial and curricular challenges that dominated early debates remain stubbornly entrenched, there are also significant promising views and practices. No longer regarded by most as a legal education outlier, clinical and experiential learning has come out of the curricular shadows and taken a prominent place in most law schools in Canada. Nuanced questions now dominate thinking around this generation of clinical and experiential learning. What is the role of community in the creation, decision making, and continuity of clinical programs? How can students balance an increasingly intensive set of learning, professional, and financial challenges? How can clinical and experiential learning be better aligned with the rest of the curriculum, and as accessible as possible? As all respondent law schools but one are expanding their clinical and experiential learning options, these and other questions will continue to animate programs in the foreseeable future.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".