The Study and Teaching of Canadian Health Law and Policy
Bibliographic record
Abstract
Much scholarly writing in the field of health law and policy asks whether health law coheres as a field of law. This chapter begins with a different set of questions more relevant to the readers of this text. How do we teach health law when it does not cohere around the traditional attributes of a legal field? What do we seek to achieve in the study of health law?\nThese questions are asked more in the spirit of exploration than in an effort to problem-solve given that the study and teaching of health law and policy in Canadian law schools is strong and diverse. Almost 100 health law courses are listed in common and civil law programs in 23 law schools across Canada. These courses capture a range of subject matter, theory and method, revealing great diversity in what is understood by the terms "health" and "law" in Canadian legal education.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.057 | 0.044 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 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".