The Social Construction of Global Health Priorities: An Empirical Analysis of Contagion in Bilateral Health Aid
Bibliographic record
Abstract
Abstract Donors of development assistance for health typically provide funding for a range of disease focus areas, such as maternal health and child health, malaria, HIV/AIDS, and other infectious diseases. But funding for each disease category does not match closely its contribution to the disability and loss of life it causes and the cost-effectiveness of interventions. We argue that peer influences in the social construction of global health priorities contribute to explaining this misalignment. Aid policy-makers are embedded in a social environment encompassing other donors, health experts, advocacy groups, and international officials. This social environment influences the conceptual and normative frameworks of decision-makers, which in turn affect their funding priorities. Aid policy-makers are especially likely to emulate decisions on funding priorities taken by peers with whom they are most closely involved in the context of expert and advocacy networks. We draw on novel data on donor connectivity through health IGOs and health INGOs and assess the argument by applying spatial regression models to health aid disbursed globally between 1990 and 2017. The analysis provides strong empirical support for our argument that the involvement in overlapping expert and advocacy networks shapes funding priorities regarding disease categories and recipient countries in health aid.
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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 | 0.009 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".