Stretching health diplomacy beyond ‘Global’ problem solving: Bringing the regional normative dimension in
Bibliographic record
Abstract
The importance of the regional dimension of health diplomacy is only gaining slow and uneven recognition. This is in many ways surprising. As demonstrated in the work of Deacon on the 'globalization of social policy', global social policy has been animated and debated not only at the multilateral level but at the regional level as well. But at least in the diplomatic literature, the importance of this regional dynamic (with a focus on diverse sites and actors and the pursuit of democratic control) has been missed. The objective of this article is to explore whether health diplomacy is catching up to this larger debate re-shaping the conceptualization and practice of diplomacy more generally. In some ways, the results may be counter-productive in that this shift may encourage an increasingly fragmented process. Yet, it may also point to some breakthroughs, with diplomats, acting as 'go to' personnel on the front lines of operational activity, enabling actors to integrate with one another to produce effective governance. In doing so, the regional dimension is given greater recognition as a component of health diplomacy, albeit in an uneven and sometimes awkward manner. Whereas global diplomacy generally emphasizes problem solving, the regional dimension is animated by a normative orientation.
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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 | 0.022 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.058 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.005 | 0.011 |
| 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".