POLÍTICAS PÚBLICAS EFICIENTES PARA O NOVO CORONAVÍRUS NO MUNDO
Bibliographic record
Abstract
New Coronavirus (COVID 19) is a pandemic and, among its characteristics, two stand out: its morbidity above the average of viruses and its media capacity. In Brazil, the word “coronavirus”, between the 15th and 21st of March, reached the index 100 on a scale of0 to 100 on Google trends, in Italy it had an index of 64, in the United Kingdom 61, in the United Arab Emirates 52, in France 50, in the United States 48, in Singapore 31, in Sweden 19, in Japan 12, in South Korea 8 and, there are no data on China. The response to the pandemic was regional, each country or, in some cases, each state, province, and city reacted differently to the same challenge. This study looks at the effect of government actions in each country. In a quantitative analysis, using officialdata, it was verified the number of infected and deaths, the restrictions imposed and, at the same time, the effects of it on the level of economic activity in these countries, such as market values. In addition, it was investigated the correlation between effect and cause, in the first quarter of 2020, in the above countries. It was found a strong relation between quarantine and acute recessions, but not between quarantine and a decreased new Coronavirus progression. We conclude that there is a need for an in-depth debate about health systems, information shared between countries and the way COVID 19 could be seen in economic statistics for decades, as it happened with World War I, the Spanish flu and World War II, however, without the global demographic impact characteristic of these three events.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".