Teaching and learning in Canadian legal education: an empirical exploration
Bibliographic record
Abstract
This dissertation is an exploration of law teaching in Canada. Through an empirical study, it aims to describe the teaching and evaluation methods used in Canadian law faculties, and to explain the pedagogical choices of law professors. The findings suggest that the dominant method of teaching in Canadian law faculties is lecturing, although this is often used with other, more interactive methods such as discussion, question-answer or some form of Socratic method. The findings also suggest that law professors' pedagogical choices are influenced by their conception of teaching, as well as other factors such as institutional requirements, culture, and students. Finally, by comparing the teaching and learning literature with the findings, the dissertation concludes that if we want to improve student learning in legal education, we must make learning the focus of teaching. In order for this to happen, we should also pay close attention to law teachers' conceptions of teaching and to their teaching context.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.027 | 0.007 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".