Teaching 'Public Interest Vocationalism': Law as a Case Study
Bibliographic record
Abstract
In this short essay, we present law as a case study of teaching professionalism in the public interest. Our hope is that the accountancy profession, as well as other professions (including law), will be prompted to reflect on the potential for the concept of public-interest vocationalism to at least inform, if not transform, education in their domains. The argument proceeds in three stages. In Part I, we set the context by identifying a number of profound challenges now facing Canadian legal education. In Part II, we introduce the concept of, and provide a justification for, public-interest vocationalism. In Part III, we provide a model of how legal education could be reformed in order to reflect, accommodate, and engender public-interest vocationalism.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.038 | 0.024 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.013 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".