Reconsidering Legal Pedagogy: Assessing Trigger Warnings, Evaluative Instruments, and Articling Integration in Canada’s Modern Law School Curricula
Bibliographic record
Abstract
Law schools are rethinking the form of instruction and the means of delivery, a discussion now at the fore of legal education. This pedagogical picture is not complete without understanding students’ fidelity to the human and social experience of law school. To further understand student experiences, a voluntary online survey was distributed to 103 first-year law students. Our findings on the use of trigger warnings, the use of 100 percent final examinations, and the integration of articling and clinical-based skills in law school education present an opportunity for law teachers to reconsider curriculum reform and conventional legal education. Legal curricula ought to contextualize law in its social impacts and this includes recognizing student experiences of trauma and vulnerability in the law classroom. Further, this recognition develops and supports important clinical skills, including participation, group work and deployment of empathy in legal settings. By recognizing student sensitivity and by implementing multiple assessments and skills-based learning and training, we argue that educators and students can work together towards common goals which benefit both the teaching and the learning of law.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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 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".