Emergent patterns in global health diplomacy: a network analysis of the resolutions adopted by the World Health Assembly from 1948 to 2022
Bibliographic record
Abstract
From a complexity perspective on governance, multilateral diplomacy is based on interactions between people, ideas, norms, policies and institutions. This article uses a computer-assisted methodology to better understand governance systems as a network of norms. All World Health Assembly (WHA) resolutions that were available from 1948 to 2022 were collected from the WHO Institutional Repository for Information Sharing (IRIS) database. Regular expressions were used to identify how resolutions cite other resolutions and the resulting relationships were analysed as a normative network. The findings show that WHA resolutions constitute a complex network of interconnected global health issues. This network is characterised by several community patterns. While chain-like patterns are associated with specific diseases programmes, radial patterns are characteristic of highly important procedural decisions that member states reaffirm in similar situations. Finally, densely connected communities correspond to contested topics and emergencies. While these emergeng patterns suggest the relevance of using network analysis to understand global health norms in international organisations, we reflect on how this computational approach can be extended to provide new understandings of how multilateral governance systems work, and to address some important contemporary questions about the effects of regime complexity on global health diplomacy.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".