The Global Fund: Anti-Corruption, Transparency and Accountability
Bibliographic record
Abstract
Abstract Background: The Global Fund to Fight AIDS, Malaria and Tuberculosis has been a key international organization in improving the health of those affected by the three big diseases. It was created during a time of health crisis and did not have the necessary anti-corruption, transparency, and accountability (ACTA) structures in place to prevent fraud and corruption in its grants, which resulted in misuse of funds by grant recipients and loss of donor confidence in 2011. Almost one decade later, this paper seeks to describe the ACTA mechanisms within the Global Fund and their results. Results: At the highest level, the Board of Directors has restructured the Global Fund’s governing committees in 2011 and in 2016 to its current Audit and Finance, Ethics and Governance, and Strategy Committees. This has helped to delineate committee mandates and to strengthen the Board’s oversight and direction on operations. In addition, the Global Fund has adopted a rigorous risk management framework and has worked risk mitigation into all aspects of 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 ethics considerations within the Global Fund. The Office of the Inspector General of the Global Fund has been effective in performing internal and external audits and investigations on fraud and corruption, suggesting changes to mitigate future risks, and implementing novel initiatives, such as the I Speak Out Now! campaign to encourage whistleblowing and to educate on signs of fraud and corruption. Finally, the “eyes and ears” of the Global Fund, the Local Fund Agents, have been involved in exposing fraud and corruption during the implementation of Global Fund grants. These mechanisms have reduced grant-related risks and procurement fraud in particular. Conclusions: Over the past decade, the Global Fund has developed a number of ACTA mechanisms. It will be critical that the Global Fund continues to monitor and evaluate how effective these mechanisms are and to make changes, when and where needed.
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 | 0.031 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.020 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".