BUSINESS IN THE CONDITIONS OF COVID-19 PANDEMIC: LEGAL REGULATION OF STATE SUPPORT
Bibliographic record
Abstract
The article analyzes the legal regulation of state support for entrepreneurship. The author notes that during the years of the economy, states are increasingly affected by global financial crises, which exhaust them and force them to look for new ways to support, rational and economic use of budget funds. Large businesses adapt quickly to the challenges of crises, but small businesses do not always respond adequately to them and are the most vulnerable, since small businesses usually do not have the financial reserves to stay afloat and avoid bankruptcy. It is proved that small and medium-sized businesses have suffered significant losses due to the introduction of quarantine due to COVID-19. the experience of Canada is analyzed, in particular, one of the components of its global economic success is a strong sector of small and medium-sized businesses. The conclusion is made about the need for effective state support, as well as in financial, small businesses, creating unfavorable conditions and a healthy competitive environment, unfavorable investments, innovations, tax and price regime and ensuring your protection during the financial crisis. It is proved that in the period 2020-2021, measures were taken to develop the strengthening of enterprises by increasing the organizational capabilities of organizations, supporting the transfer of knowledge and technologies, modernizing the existing infrastructure to support joint ventures by participating in international educational and training programs. It is noted that in order for the state to improve financial regulation of enterprises, first of all, Ukraine needs to continue to strengthen its institutional base to create favorable conditions for the growth of medium-sized businesses.
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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 | 0.005 | 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 | 0.000 | 0.001 |
| 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".