Reversal of Fortune: Litigating Health Care Reform in Auton v. British Columbia
Bibliographic record
Abstract
In June 2004 the Supreme Court heard oral arguments in Chaoulli v.Quebec (Attorney General) and Auton (Guardian ad litem of) v. British Columbia (Attorney General). at issue in Chaoulli was the constitutionality of legal restrictions on the private provision of health care; at issue in Auton was the constitutionality of British Columbia’s decision not to fund a specific treatment for autism within its public health care system. Chaoulli and Auton are the most visible manifestations of an increasingly common phenomenon: The use of rights-based litigation as an instrument of health care policy reform. This paper focuses on Auton as an example of legal mobilization. It explores three key questions about the phenomenon of planned, strategic litigation: How these cases enter the judicial system? Under what conditions are they likely to be successful? What is the impact of winning — or losing — on the broader policy environment? the paper concludes with some general comments on the use of litigation for health care policy reform.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.030 | 0.014 |
| Scholarly communication | 0.013 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.016 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 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".