Global Health: A Pivotal Moment Of Opportunity And Peril
Bibliographic record
Abstract
A growing tide of populism in Europe and the United States, combined with other factors, threatens the solidarity upon which the global health movement is based. The highest-profile example of the turn toward populism is US president-elect Donald Trump, whose proposals would redefine US engagement in global health, development, and environmental efforts. In this challenging landscape, three influential global institutions-the United Nations, the World Health Organization, and the World Bank-are undergoing leadership transitions. This new global health leadership should prioritize global health security, including antimicrobial resistance, health system strengthening, and action on mass migration and climate change. They will need to work as a team, leveraging the World Health Organization's technical competence and mandate to set health norms and standards, the United Nations' political clout, and the World Bank's economic strength. Human rights, including principles of equality, participation, and accountability, should be their foremost guide, such as holding a United Nations special session on health inequities and advancing the Framework Convention on Global Health. The need for predictable and innovative financing and high ethical standards to prevent conflicts of interest can further guide global health leaders.
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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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.027 |
| Scholarly communication | 0.030 | 0.031 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.015 | 0.021 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".