COVID-19 Pandemic as an Excellent Opportunity for Global Health Diplomacy
Bibliographic record
Abstract
Undoubtedly, the COVID-19 pandemic is not the first and most frightening global pandemic, and it may not be the last. At the very least, this phenomenon has though seriously challenged the health systems of the world; it has created a new perspective on the value of national, regional, and international cooperation during crises. The post-coronavirus world could be a world of intensified nationalist rivalries on the economic revival and political influence. However, strengthening cooperation among nations at different levels will lead to the growth of health, economy, and security. The current situation is a touchstone for international actors in coordinating the efforts in similar future crises. At present, this pandemic crisis cannot be resolved except through joint international cooperation, global cohesion, and multilateralism. This perspective concludes that the pandemic could be an excellent opportunity for the scope of global health diplomacy (GHD) and how it can be applied and practiced for strengthening five global arenas, namely (1) International Cooperation and Global Solidarity, (2) Global Economy, Trade and Development, (3) Global Health Security, (4) Strengthening health systems, and (5) Addressing inequities to achieve the global health targets. GHD proves to be very useful for negotiating better policies, stronger partnerships, and achieving international cooperation in this phase with many geopolitical shifts and nationalist mindset among many nations at this stage of COVID-19 vaccine roll-out.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".