Locating health diplomacy through African negotiations on performance-based funding in global health
Bibliographic record
Abstract
This article examines how national health actors in South Africa, Tanzania and Zambia perceive the participatory quality of negotiation processes associated with the performance-based funding mechanisms of the Global Fund to Fight AIDS, Tuberculosis and Malaria and the World Bank.Through analysis of qualitative fieldwork consisting of 101 interviews within the case countries as well as in Geneva and Washington DC, the research results show that African actors within national governments generally set and negotiate performance targets of performance-based funding schemes.Nevertheless, the results also show that the quality of those negotiations with external funders were inconsistent, suggesting the existence of asymmetrical power and influence in relation to the quality of those negotiations.This raises questions about the level of power and influence being exerted by external funders and how much negotiation leverage African political actors have available to them within global health diplomacy.It also provides evidence that certain key aspects of these negotiated processes are closed off from negotiation for African actors and therefore undermine African participation in significant ways.Witter, S. et al. (2012) 'Paying for performance to improve the delivery of health interventions in low-and middle-income countries', Cochrane Database of Systematic Reviews, Issue 2, Art.No.:CD007899.
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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 | 0.026 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.020 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.001 | 0.020 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.027 | 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".