Telling Tales About Law School: Reflections on Care, Holism, and Marginality in Law Teaching
Bibliographic record
Abstract
In this reflective piece, we explore why and how adopting principles of holistic education can help mitigate the crisis of well-being in legal education and in the legal profession. This project emerged from a series of informal conversations in which we discussed various factors influencing our pedagogical styles and found a shared central essence in both our approaches that align with holistic teaching. Given that this paper emerged through subjective conversation, we present subjective narratives about our experiences as early-career faculty members at Canadian law schools, and then critically analyze the narratives by distilling common emergent themes and threads relating to wellness, teaching with care, marginality, and whole-person (holistic) education generally. Within the context of these themes, we discuss potential avenues for making teaching and learning about law more enriching. Our hope is that this paper serves as an invitation to other academics to contribute their own narratives and stories to the effort toward deeply meaningful higher education. While some of our comments are particularized to the law school context as it is our realm of experience, the broader themes raised here are relevant across disciplines.
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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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.053 | 0.079 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.006 | 0.017 |
| Insufficient payload (model declined to judge) | 0.004 | 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".