The landscape of public-private partnerships in global health governance: introducing a new dataset
Bibliographic record
Abstract
BACKGROUND: Global health public-private partnerships are prominent actors and forums for the governance of global health. They channel significant funding into global health and shape policy priorities and options for pressing health problems. Led by state and non-state actors, they are often championed as inclusive governing spaces. Despite their prominence, there is no up-to-date, comprehensive analysis of the quantity and qualities of global health public-private partnerships, including the distribution of decision-making power among their governing board members. RESULTS: This article analyzes a new dataset of 73 global health public-private partnerships governed by a total of 630 actors. These analyses offer three high-level insights. First, high-income country representatives hold 69% of seats on partnership governing boards. Thus, while public-private partnerships have expanded the types of actors that can participate in governance, there remain significant disparities in access to decision-making based on country income-level. Second, a typology of public-private partnerships based on the composition of decision-makers on governing boards is presented. The typology includes Business, Civil Society, Trio, and Super public-private partnerships, of which Trio and Civil Society partnerships are the most common. Third, as public-private partnerships themselves hold governing seats in 24 partnerships, this article lends support to the idea that some partnerships are gaining agency and autonomy in global health through inter-partnership cooperation. Additional analyses shed light on the timeline of the rise of public-private partnerships and a range of characteristics, including their headquarter location, function, health issues addressed, and legal status. CONCLUSIONS: This article provides a big picture perspective on key patterns in the characteristics and distribution of decision-making power of global health public-private partnerships. Together, the analyses suggest that moving from multilateral governance through international organizations like the World Health Organization, to multistakeholder governance through public-private partnerships has contributed to a decrease in decision-making influence for low and middle-income countries and an increase for high-income countries. In doing so, it lays the groundwork for scholarly and practitioner debate about the appropriate distribution of decision-making power in global health governance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".