Economic Impacts of Covid-19 Pandemic (Country and Global Perspective)
Bibliographic record
Abstract
The pandemic of 2020 caught many by surprise. In spite of regional outbreaks of epidemics with lethal consequences such as SARS or Ebola, for about a century the mankind has not faced such a global pandemic as hit the globe during the first quarter of 2020. However, in the history, there have been other pandemics, which took the death toll of millions of people such as Black Death in 14th century, or the 1918 Fluxxxv, which probably killed more people than the World War I taking place at about the same time (Learn, 2020). The experience shows that during these pandemic periods, mistakes were also made, such as the failure of people to socially distance, which we can learn from even today. During the Spanish flu, the spread of the virus was also caused by the migration of solders during the war. Thus, the past experience also documents that the spread of the virus can be contained by reduced mobility of population, both within individual countries and internationally. However, it is also equally clear that the containment measures aimed to reduce the mobility are also very costly, have substantial consequences on the economy of the country and create recessionary pressures.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".