The changing role of health‐oriented international organizations and nongovernmental organizations
Bibliographic record
Abstract
Apart from governments, there are many other actors active in the health policy arena, including a wide array of international organizations (IOs), public-private partnerships and non-governmental organizations (NGOs) that state as their main mission to improve the health of (low-income) populations of low-income countries. Despite the steady rise in numbers and prominence of NGOs, however, there is lack of empirical knowledge about their functioning in the international policy arena, and most studies focus on the larger organizations. This has also caused a somewhat narrow focus of theoretical studies. Some scholars applied the 'principal-agent' theory to study the origins of IOs, for example, other focus on changing power relations. Most of those studies implicitly assume that IOs, public-private partnerships and large NGOs act as unified and rational actors, ignoring internal fragmentation and external pressure to change directions. We assert that the classic analytical instruments for understanding the shaping and outcome of public policy: ideas, interests and institutions apply well to the study of IOs. As we will show, changing ideas about the proper role of state and non-state actors, changing positions and activities of major stakeholders in the (international) health policy arena, and shifts in political institutions that channel the voice of diverging interests resulted in (and reflected) the changing positions of the health-oriented organizations-and also affect their future outlook. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.015 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".