Bibliographic record
Abstract
The article presents the results of a study of the world experience of using public finances in the context of the spread of the COVID-19 pandemic and evaluates domestic measures of anti-crisis support of the national economy. It was noted that the volume and forms of financial support depended on the depth of the negative consequences of a pandemic in a country, as well as on the solvency of governments, their reputation as borrowers. Developed countries had the opportunity to approve fiscal support measures for several years, while in developing countries, such measures were short-term. It is emphasized that in these conditions the biggest concern is the growing budget deficit and public debt. The total package of anti-crisis measures of Ukraine, which is estimated at UAH 111.2 billion (2.8% of GDP), is analyzed. It is noted that this package is significantly inferior to the volume of support programs in developed countries, which reached levels ranging from 5-10% of GDP (China, UK, USA) to 18.8% of GDP (Canada) and even 23% of GDP (France). It was noted that despite the fact that the implementation of the "Big Construction" program has become the largest anti-crisis instrument in 2020, which undoubtedly had a positive effect and impact on economic indicators, it should not be considered an anti-crisis measure. This program was planned before the deployment of the COVID-19 pandemic, it is not directly related to the direct support of the population and compensation for business losses, so it cannot be considered a government response to the COVID-19. It is concluded that the expected continuation of the pandemic in 2021 will require governments to pursue policies to support business and households. In these circumstances, the Ukrainian government will be required to increase the volume of anti-crisis measures. At the same time it is necessary to observe prudence and caution in choosing the sources of government borrowing. For the Ukrainian state, this task is complicated by the fact that overcoming the crisis must be carried out in the context of continuing reforms, strengthening the imbalance of public finances, increasing macroeconomic instability.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".