The state of global health in a radically unequal world: patterns and prospects
Bibliographic record
Abstract
Introduction Sir Michael Marmot, who chaired the World Health Organization (WHO) Commission on Social Determinants of Health, has identified the need to seek “public policy based on a vision of the world where people matter and social justice is paramount” (Marmot, 2005, p. 1099). In this chapter, we ground this imperative in evidence of dramatic disparities in health status that are traceable, in large measure, to the globally unequal distribution of resources necessary for health. We further outline the contours of an international economic and political order that often magnifies those inequalities, and conclude that the imperative of mobilizing resources to protect health on a much larger scale than at present is central to any global health ethics worthy of the name. “If living were a thing that money could buy” Imagine for a moment a series of disasters that killed almost 1400 women every day for a year: the equivalent of four or five daily crashes of crowded long-distance airliners. There is little question that such a situation would quickly be regarded as a humanitarian emergency, as the stuff of headlines, especially if ways of preventing the events were well known and widely practised in some parts of the world. However, remarkably little attention is paid outside the global health and human rights domains to complications of pregnancy and childbirth that kill more than 500,000 women every year – a cause of death now almost unheard-of in high-income countries (HICs).
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".