(In)sufficient institutionalization? Norm articulation in the World Health Organization and infectious disease prevalence across the global South
Bibliographic record
Abstract
Recent work in the neoinstitutional tradition has sought to clarify the mechanisms by which global norms diffuse across the world system. Prior work highlights the role of organizational linkages between world society and the nation-state—especially international nongovernmental organizations (INGOs)—in the process of spreading policies, practices, and ideas cross-nationally. Although prior empirical studies ask whether diffusion occurs, this study examines the conditions under which such effects are stronger versus weak (or absent). To do so, we use the strategic case of norm articulation in the World Health Organization (WHO) and its relationship to infectious disease prevalence across the global South. Our research design leverages variation in the extent to which issues garner attention within this intergovernmental organization. We begin by identifying four infectious diseases with variable degrees of prominence on the WHO agenda. In the descending order, they are HIV, tuberculosis, leprosy, and Guinea-worm disease. We then estimate the impact of organizational links to world society (operationalized as health INGOs) on disease prevalence and compare results across each of the four outcomes. Results support the neoinstitutional argument that diffusion is conditional on the extent to which norms are articulated in the prevailing global institution. We find that, for the most part, world society links are associated with lower rates of infectious disease. However, the size and significance of the relationship depends on a disease’s relative priority on the WHO agenda. In the absence of sufficient norm articulation, results show that integration into world society is unrelated to infectious disease prevalence.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".