Exploring anti-corruption, transparency, and accountability in the World Health Organization, the United Nations Development Programme, the World Bank Group, and the Global Fund to Fight AIDS, Tuberculosis and Malaria
Bibliographic record
Abstract
Corruption is recognized by the global community as a threat to development generally and to achieving health goals, such as the United Nations Sustainable Development Goal # 3: ensuring healthy lives and promoting well-being for all. As such, international organizations such as the World Health Organizations and the United Nations Development Program are creating an evidence base on how best to address corruption in health systems. At present, the risk of corruption is even more apparent, given the need for quick and nimble responses to the COVID-19 pandemic, which may include a relaxation of standards and the rapid mobilization of large funds. As international organizations and governments attempt to respond to the ever-changing demands of this pandemic, there is a need to acknowledge and address the increased opportunity for corruption.In order to explore how such risks of corruption are addressed in international organizations, this paper focuses on the question: How are international organizations implementing measures to promote accountability and transparency, and anti-corruption, in their own operations? The following international organizations were selected as the focus of this paper given their current involvement in anti-corruption, transparency, and accountability in the health sector: the World Health Organization, the United Nations Development Program, the World Bank Group, and the Global Fund to Fight Aids, Tuberculosis and Malaria. Our findings demonstrate that there has been a clear increase in the volume and scope of anti-corruption, accountability, and transparency measures implemented by these international organizations in recent years. However, the efficacy of these measures remains unclear. Further research is needed to determine how these measures are achieving their transparency, accountability, and anti-corruption goals.
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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 | 0.033 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".