Viewing Health as a Human Right: On the Normative Framework Linking Health to Human Rights
Bibliographic record
Abstract
The current global health literature is skeptical of the idea that one can defensibly claim a moral right to health. Gopal Sreenivasan and Onora O'Neill argue that a positive right to health is fraught with conceptual difficulties because it is unclear who bears the correlative duty to secure the right. Jonathan Wolff has recently attempted to provide a normative foundation for the human right to health from a non-cosmopolitan point of view, but his account fails to directly address Sreenivasan and O'Neill's objections. In this paper, I will develop and further substantiate Wolff's position in an attempt to respond to Sreenivasan and O'Neill's critique of a positive right to health. I will argue that Wolff unknowingly seems to be making a case for a negative right to health, which I conclude provides a non-cosmopolitan normative foundation for the human right to health.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.013 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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; both teacher heads agree on what is shown here.
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".