India’s Neighbourhood Vaccine Diplomacy During COVID-19 Pandemic: Humanitarian and Geopolitical Perspectives
Bibliographic record
Abstract
In recent years, India has established itself as the world’s ‘pharmacy hub’, and this claim was proven once again when it delivered COVID-19 vaccines to its citizens, neighbouring nations and across the globe. Following the philosophy of humanitarianism through the principle of ‘Vasudhaiva Kutumbakam’, India has decided to provide the COVID-19 health assistance to its immediate neighbouring countries. India’s immediate neighbourhood refers to the countries that are geographically adjacent to it. In addition, India’s vaccine diplomacy has exposed geopolitical fault lines in South Asia as China’s vaccine diplomacy aims to outpace India in the region. Against this background, the main objective of this paper is to explain and examine India’s vaccine diplomacy as an instrument of its ‘Neighbourhood First’ policy during the COVID-19 pandemic. It argues that India’s health-focused approach has proved effective and aligned with its national interests. This review demonstrates that India’s health diplomacy has had an impact on medical and humanitarian assistance reciprocation at the regional and international levels. As a result of this strategy, during the second wave of the pandemic, India received medical devices and vaccines from other countries in dealing with COVID-19.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".