Disrupting Business as Usual: Considering Teaching Methods in Business Law Classrooms
Bibliographic record
Abstract
The Truth and Reconciliation Commission of Canada (TRC)’s Calls to Action propose signimcant changes to legal education. No law school classroom is exempt, including business law courses. We are two of a growing number ofscholars in the legal academy actively incorporating Indigenous laws, critical race theory and socio-economic perspectives into business law courses as part of our responses to the TRC. This paper explores a field school we developed at Windsor Law as a response to the Calls to Action. In a temporary fusion of two courses, Secured Transactions along with Indigenous Peoples, Art & Human Rights, a synergy emerges through “collaterization” and “valuation.” Our methodology of combining courses and students with diverging interests was designed to evoke reflections on the intersections of Indigenous law and commercial law in legal education. In closing we offer five ways in which business law classrooms might respond to the TRC recommendations.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".