The Rise of Global Health: Consensus, Expansion and Specialization
Bibliographic record
Abstract
This dissertation examines the rise of global health assistance among states, multilateral institutions and NGOs. Resources devoted to global public health expanded rapidly in the 1990s and 2000s, outpacing other areas of development. New agencies have emerged to address public health issues, and existing organizations such as the UNDP, World Bank and EU have expanded their global health operations. Critics fear that the global health regime will become inefficient as it grows, duplicating tasks and skewing resources. The regime complex literature predicts similar suboptimal outcomes. These fears are overblown. While certain inefficiencies are likely as any regime expands, data shows that the allocation of resources generally reflects global health needs. Increased competition, thought to lessen efficiency, has actually pressured multilateral actors to specialize. Specialization offsets the problem of overlapping tasks. The modern global health regime is characterized by increased size, competition, specialization, and a prevailing consensus that emphasizes health as a central component of international development. This consensus holds that societal health prefigures economic growth. The international community, moreover, should cost effectively use increased aid to address the worst disease burdens in the poorest countries. In the cases of states, domestic interests play a role in shaping specialization patterns. Pressure from increased international competition has pressed multilateral institutions to reform and adapt to changing conditions in order to remain relevant in a denser global environment. The diverse cases explored in this dissertation (US, Japan, Sweden, Canada, World Bank, WHO, UNDP and EU) show high degrees of specialization and a surprisingly similar adherence to the consensus.
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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 | 0.024 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.006 | 0.041 |
| Scholarly communication | 0.016 | 0.028 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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".