The challenge of middle-income countries to development assistance for health: recipients, funders, both or neither?
Bibliographic record
Abstract
Recent developments have transformed the role and characteristics of middle-income countries (MICs). Many stakeholders now question the appropriate role of MICs in the system of development assistance for health (DAH), and key funders have already recast their approach to these countries. The pressing question is whether MICs should be recipients, funders, both or neither. The answer has deep implications for individual countries and their citizens, and for the DAH system as a whole. We clarify the fundamental issues involved and emphasise a special feature of many MICs: mid-level gross national income per capita (GNIpc) combined with substantial health needs and large inequalities. We discuss the trade-off between concerns for capacity and need, and illustrate a capacity-based approach to setting the level of a GNIpc eligibility threshold. We also discuss how needs-based exceptions and incentive-preserving instruments can complement such a threshold. Against this background, we outline options for the future roles of MICs in various circumstances. We conclude that major players in the DAH system have reason to reconsider the criteria for allocating DAH among countries and the norms for which countries should contribute and how much.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.043 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.026 |
| Scholarly communication | 0.023 | 0.017 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.014 | 0.014 |
| Insufficient payload (model declined to judge) | 0.009 | 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".