The Global Fund: why anti-corruption, transparency and accountability matter
Bibliographic record
Abstract
BACKGROUND: The creation of the Global Fund to Fight AIDS, Tuberculosis and Malaria, also known as the Global Fund, was prompted by the lack of a timely and effective global response, and the need for financing to fight against three devastating diseases: HIV/AIDS, tuberculosis, and malaria. During the formation of the Global Fund, necessary anti-corruption, transparency, and accountability (ACTA) structures were not put in place to prevent fraud and corruption in its grants, which resulted in the misuse of funds by grant recipients and an eventual loss of donor confidence in 2011. The Global Fund has instituted various ACTA mechanisms to address this misuse of funding and the subsequent loss of donor confidence, and this paper seeks to understand these implementations and their impacts over the past decade, in an effort to probe ACTA more deeply. RESULTS: By restructuring the governing committees in 2011, and the Audit and Finance; Ethics and Governance; and Strategy Committees in 2016, the Global Fund has delineated committee mandates and strengthened the Board's oversight of operations. Additionally, the Global Fund has adopted a rigorous risk management framework which it has worked into all aspects of its functioning. An Ethics and Integrity Framework was adopted in 2014 and an Ethics Office was established in 2016, resulting in increased conflict of interest disclosures and greater considerations of ethics within the organization. The Global Fund's Office of the Inspector General (OIG) has effectively performed internal and external audits and investigations on fraud and corruption, highlighted potential risks for mitigation, and has implemented ACTA initiatives, such as the I Speak Out Now! campaign to encourage whistleblowing and educate on fraud and corruption. CONCLUSIONS: From 2011 onwards, the Global Fund has developed a number of ACTA mechanisms which, in particular, resulted in reduced grant-related risks and procurement fraud as demonstrated by the decreased classification from high to moderate in 2017, and the reduction of investigations in 2018 respectively. However, it is crucial that the Global Fund continues to evaluate the effectiveness of these mechanisms; monitor for potential perverse impacts; and make necessary changes, when and where they are needed.
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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 | 0.097 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.023 | 0.019 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.008 | 0.006 |
| 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; 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".