Bibliographic record
Abstract
This article examines the shift towards experiential legal education and its implications. While others have focused on experiential education as a means of training better lawyers, the author advances the argument for experiential education because it is rooted in substantive problem-solving, access to justice, engagement with communities, and greater opportunities for reflective and critical thinking about law and justice. Drawing on examples from Osgoode Hall Law School, which adopted an experiential curricular requirement in 2012, the article explores the ways in which experiential education may change law school and law students. The article also canvasses the implications of the experiential shift for the future of legal education, and the blurring lines between law school and transitional professional education in law such as articling and Practical/Professional Legal Training Courses (PLTCs). Finally, a number of perspectives and research initiatives are presented to suggest that the benefits of an effectively designed experiential model are far reaching, from a learning environment that caters most effectively to the way in which students learn and access information, to increasing engagement with community needs, to the positive impacts on student wellness. Therefore, the article illustrates the significance of the experiential shift in legal education in the Canadian context as a critical driver in the evolution of the law school and professional legal education.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".