The right to health at the public/private divide: a global comparative study
Bibliographic record
Abstract
Through a comparative global study of countries from all continents representing a diversity of health, legal, political, and economic systems, this book explores the role of health rights in advancing greater equality through access to health care. Does health care promote equality, or does it in fact advance the opposite result? Does inserting the idea of 'the right to health' into health systems allow the reinsertion of public values into systems that are undergoing privatization? Or does it allow for private claims to be re-articulated as 'rights', in a way that actually reinforces inequality? This volume includes studies from countries such as the United States, United Kingdom, Brazil, Canada, The Netherlands, China, and Nigeria, among many others, and authors with expertise in the legal and health systems of their countries, making this a seminal study that allows readers to see the differing role of rights in various health systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".