Donor funding priorities for communicable disease control in the developing world
Bibliographic record
Abstract
Prior research has considered donor funding for developing world health by recipient and donor country but not by disease. Examining funding by disease is critical since diseases may be in competition with one another for priority and donors may be making allocation decisions in ways that do not correspond to developing world need. In this study I calculate donor funding for 20 historically high-burden communicable diseases for the years 1996 to 2003 and examine factors that may explain variance in priority levels among diseases. I consider funding for developing world health from 42 major donors, classifying grants according to the communicable disease targeted. Data show that funding does not correspond closely with burden. Acute respiratory infections comprise more than a quarter of the burden among these diseases but receive less than 3% of direct aid. Malaria also stands out as a high-burden neglected disease. The evidence indicates that neither developing world need nor industrialized world interests explain all funding patterns, and that donors may be imitating one another in ways that do not take into account problems in the developing world. There is an urgent need for a major increase in funding for communicable disease control in the developing world, and for more balanced allocation of the resources already provided.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".