The Need for Shared Health Governance, Mutual Collective Accountability, and Transparency in COVAX
Bibliographic record
Abstract
This study addressed the question: to what extent does COVAX employ shared health governance, mutual collective accountability, and transparency? We conducted a multi-method qualitative study triangulating document analysis and key informant interviews. Data was analyzed using qualitative content analysis. Results demonstrate that each of COVAX’s co-convening organizations are governed by and formally accountable to their individual boards. This structure for accountability, however, is ineffective when decisions are made collaboratively. As a result, most effective accountability for COVAX comes from informal accountability mechanisms such as media scrutiny. Furthermore, COVAX lacks transparency and has not achieved its goals to date. These results demonstrate that COVAX does not employ shared health governance or mutual collective accountability. These results also illuminate barriers to successful global collaboration and contribute to the literature about the roles of good governance, transparency, and accountability in global health initiatives.
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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 | 0.064 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.038 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.003 | 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".