World market development scenario in the context of the coronavirus crisis
Bibliographic record
Abstract
The purpose of this research is to determine the global market development scenarios as a result of the influence of the COVID-19 virus, and also to determine to the extent possible the consequences for the global market. To establish the various effects of the coronavirus on the economy and protective equipment, as well as probable transmission channels. Methods. Mathematical, empirical, systemic, analytical, economic and other approaches are used to study the development of the world market in the conditions of the coronavirus disease. Results. We give a brief description of the Kermack–MacKendrick epidemic model, corresponding to the general nature of the current coronavirus epidemic, that can dramatically change the global market development scenario. We show three scenarios for the global economy development. Quick recovery implies a slowdown in economic growth in the United States and Europe will end by the end of March; China is likely to recover by the end of April, and demand will recover relatively quickly. Global slowdown implies the economy will recover at the end of the second quarter, but global GDP growth in 2020 will drop to 1...1.5 percent. Global pandemic implies a serious shock to the global economy, which can last for almost a year. Conclusion. Three scenarios of the world economy development in the context of the coronavirus crisis are formulated, as well as the various effects of the coronavirus disease on the world economy are identified; remedies are proposed. We concluded that the coronavirus disease will affect microeconomic heritage, macroeconomic heritage and political heritage. Multilaterally, the crisis can be interpreted as a call for increased cooperation or, on the contrary, a need to expand the bipolar centers of geopolitical power. We list a number of mathematical papers with extensive bibliography on COVID-19. On the example of one of such works we have shown that the Kermack–Mac-Kendrick model remains the backbone of the research in this sphere.
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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 | 0.001 | 0.001 |
| 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 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".