Bibliographic record
Abstract
BACKGROUND: A world divided by health inequalities poses ethical challenges for global health. International and national responses to health disparities must be rooted in ethical values about health and its distribution; this is because ethical claims have the power to motivate, delineate principles, duties and responsibilities, and hold global and national actors morally responsible for achieving common goals. Theories of justice are necessary to define duties and obligations of institutions and actors in reducing inequalities. The problem is the lack of a moral framework for solving problems of global health justice. AIM: To study why global health inequalities are morally troubling, why efforts to reduce them are morally justified, how they should be measured and evaluated; how much priority disadvantaged groups should receive; and to delineate roles and responsibilities of national and international actors and institutions. DISCUSSION AND CONCLUSIONS: Duties and obligations of international and state actors in reducing global health inequalities are outlined. The ethical principles endorsed include the intrinsic value of health to well-being and equal respect for all human life, the importance of health for individual and collective agency, the concept of a shortfall from the health status of a reference group, and the need for a disproportionate effort to help disadvantaged groups. This approach does not seek to find ways in which global and national actors address global health inequalities by virtue of their self-interest, national interest, collective security or humanitarian assistance. It endorses the more robust concept of "human flourishing" and the desire to live in a world where all people have the capability to be healthy. Unlike cosmopolitan theory, this approach places the role of the nation-state in the forefront with primary, though not sole, moral responsibility. Rather shared health governance is essential for delivering health equity on a global scale.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.024 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".