HEALTH VULNERABILITY VERSUS ECONOMIC RESILIENCE TO THE COVID-19 PANDEMIC
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it