Bibliographic record
Abstract
The COVID-19 epidemic took the world by surprise in early 2020.It was initially thought to be a China-only problem and later believed to be a South East Asian issue.However, due to various natural, political and regulatory factors, the epidemic spread rapidly to the rest of the world, causing havoc in the areas of health and the economy mainly.According to estimates by the International Monetary Fund (IMF) in 2020, the annual percentage change in world real GDP was -3.5%.In advanced economies, GDP fell on average by -4.9%.This variation was more striking in some nations than in others.For example, in the United States the fall was -3.4%, in Germany -5.4%, in France -9.0%, in Italy -9.2%, in Spain -11.1%, in Japan -5, 1%, in the United Kingdom -10.0% and in Canada -5.5%.The situation was no different for emerging and developing economies, which also saw their real GDP fall by -2.4%.This variation was more striking in some countries than in others.For example, in India the fall was -9.0%, in Russia -3.6%, in Brazil -4.5%, in Mexico -8.5%, in Saudi Arabia -3.9%, in Nigeria -3.2%, in South Africa -7.5%, and in Latin America and the Caribbean -7.4%.China was the only country that registered a positive variation of 2.3% (IMF, 2021).The drop in world GDP was also reflected in the decline in international trade, basically in the import and export of products and services.However, there was naturally an increase in trade in products and services directly related to the COVID-19 pandemic.For example, according to World Bank data,
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".