A BIBLIOMETRIC ANALYSIS OF HEALTH DIPLOMACY RESEARCH BASED ON VOSVIEWER AND CITESPACE
Bibliographic record
Abstract
Research on health diplomacy not only deepens global health governance but also enhances the sharing of information and resources in the field of public health. A bibliometric study was conducted on health diplomacy works published between 1993 and 2023 with “health diplomacy”, “medicine diplomacy”, “health and foreign policy”, or “vaccine diplomacy” as the keywords. VOSviewer and CiteSpace were used to perform the bibliometric analysis. A total of 2,216 articles from the Web of Science database were analyzed. Results found that the United States held a prominent and influential position in health diplomacy studies, followed by China, the United Kingdom, and Australia. The London School of Hygiene & Tropical Medicine, the University of Toronto, and Harvard University were the top three research institutes for health diplomacy. The article from Feldbaum et al. (2010) served as the representative and symbolic reference. These findings showed that topics including power, Covid-19, security, soft power, WHO, vaccine diplomacy, and governance, though with shorter spans, were the focal points in recent years. In addition, health diplomacy research exhibited interdisciplinary, cross-cutting, and temporal characteristics closely related to factors such as politics, economics, environment, and public goods.
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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 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.319 | 0.357 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".