Evaluating official development assistance-funded granting mechanisms for global health and development research that is initiated in high-income countries
Bibliographic record
Abstract
BACKGROUND: Several countries allocate official development assistance (ODA) for research on global health and development issues that is initiated in the donor country. The integration of such research within domestic research systems aligns with efforts to coordinate ODA investments with science, technology and innovation policies towards achieving the Sustainable Development Goals (SDGs). METHODS: Through a document synthesis and interviews with research funders in ODA donor and recipient countries, we evaluated the performance of this funding approach across seven donor-country programmes from five donor countries and examined the institutional design elements that increase its chances of advancing development goals and addressing global challenges. RESULTS: We found that carefully designed programmes provide a promising pathway to producing valuable and contextually relevant knowledge on global health and development issues. To achieve these outcomes and ensure they benefit ODA-receiving countries, programmes should focus on recipient-country priorities and absorptive capacity; translate research on global public goods into context-appropriate technologies; plan and monitor pathways to impact; structure equitable partnerships; strengthen individual and institutional capacity; and emphasize knowledge mobilization. CONCLUSIONS: Global health and development research programmes and partnerships have an important role to play in achieving the SDGs and addressing global challenges. Governments should consider the potential of ODA-funded research programmes to address gaps in their global health and development frameworks. In the absence of concrete evidence of development impact, donor countries should consider making increases in ODA allocations for research additional to more direct investments that have demonstrated effectiveness in ODA-receiving countries.
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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 | 0.581 | 0.610 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.007 | 0.017 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".