A Conversation About Canadian Legal Education: Lakehead University and Dialogue Pedagogy
Bibliographic record
Abstract
Through my experiences opening the first new law school in Ontario in 44 years, I have had time to reflect on my own teaching style and have employed what has been termed “Dialogue Pedagogy.” Using an auto-ethnography methodology in this paper, I will examine ways of teaching the law including the case-law system of study distilling black-letter law and the Socratic Method which is widely considered the preferred method in legal education. I argue that there is a better, and more humane, way to teach our law school students. Experience has shown that although students get training on issue identification, they are not taught to plan a solution by dialoguing with the relevant actors. It has been my challenge to come up with an adequate teaching and evaluation method that allows students to do an interactive assignment. I have found that law students need more opportunities to access their emotional intelligence and interactions with real people, and I have captured this skill in my mandatory 1L Tort class through an exercise I call the “Torts Crime Scene.” Connecting our students to life and humanity and having empathy for others is perhaps one of the most important things that we do. Having students learn dusty legal maxims is, for good or bad, essential. However, being able to connect what they are learning to them personally, their family, their friends, their fellow human beings is paramount. Our students have changed, and so too, our teaching must change. The transformation cannot simply be a system of add-ons but of fundamental review and evolution.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.081 | 0.019 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.020 | 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 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".