HEALTH VULNERABILITY VERSUS ECONOMIC RESILIENCE TO THE COVID-19 PANDEMIC
Bibliographic record
Abstract
The purpose of this study is to understand how countries have leveraged on their economic resilience to fight the COVID-19 pandemic. The focus is on a global sample of 150 countries. The study develops a health vulnerability index (HVI) and leverages on an existing economic resilience index (ERI) to provide four main scenarios from which to understand the problem statement, namely ‘low HVI-low ERI,’ ‘high HVI-low ERI,’ ‘high HVI-high ERI,’ and ‘low HVI-high ERI’ quadrants. Countries that have robustly fought the pandemic are those in the ‘low HVI-high ERI’ quadrant and, to a lesser extent, countries in the ‘low HVI-low ERI’ quadrant. Most European countries, namely one African country (Rwanda), four Asian countries (e.g., Japan, China, South Korea, and Thailand), and six American countries (e.g., United States, Canada, Uruguay, Panama, Argentina, and Costa Rica) are apparent in the ideal quadrant.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 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".