Denaturalizing scarcity: a strategy of enquiry for public-health ethics
Bibliographic record
Abstract
Most scarcities that underpin health disparities within and among countries are not natural; rather, they result from policy choices and the operation of social institutions. Using examples from the United States of America: the Chicago heat wave and hurricane Katrina, this paper develops "denaturalizing scarcity" as a strategy for enquiry to inform public-health ethics in an interconnected world. It first describes some of the resource scarcities that are of greatest concern from a public-health perspective, and then outlines two (not mutually exclusive) lines of ethical reasoning that demonstrate their importance. One of these involves the multiple relationships that link rich and poor across national borders in today's interconnected world. The paper then briefly describes ways in which globalization and the associated institutions are linked to health-threatening scarcities. The paper concludes that denaturalizing scarcity represents a valuable alternative to mainstream health ethics, directing our attention instead to why some settings are "resource poor" and others are not.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".