National Vulnerability to Pandemics: The Role of Macroenvironmental Factors in COVID‐19 Evolution
Bibliographic record
Abstract
The aim of this research is to show that vulnerability to pandemics is unequal across nations, and that culture besides other national factors helps unleash some of the disparities. A nation's vulnerability to pandemics is defined as a state of fragility and dereliction at the national level perceptible at the early stage of the emergence of a pathogen when no definite information is available about it and no clear response is in place, creating a dependence on national factors as well as contextual factors. That is, vulnerability reflects the evolution or spread of a nascent pandemic in a given nation. A set of hypotheses that prescribe how a nation's factors would contribute to its vulnerability is developed. Data reflecting the national factors of a sample of countries that reported early COVID-19 cases were collected from secondary sources to test the hypotheses. The results show that, whereas factors such as economy and healthcare had a modest effect, two cultural factors were salient in shaping a nation's vulnerability to COVID-19. Furthermore, poverty prevalence associated with a nation's vulnerability. Delineating how a nation's culture and macroenvironmental factors shape its vulnerability at early stages of pandemic evolution, the results encourage policymakers to extend timely support to nations high on uncertainty avoidance and low on indulgence, as well as where poverty is prevalent. Such nations require proactive measures such as behavioral interventions and communications that are culturally sensitive and inclusive.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".