SMALL BUSINESS DEVELOPMENT IN UKRAINE: CURRENT CHALLENGES AND PROSPECTS
Bibliographic record
Abstract
Russia's full-scale invasion of Ukraine has caused significant challenges for business in Ukraine, especially for small businesses. At the same time, small businesses in the fourth year of the full-scale invasion demonstrate resilience and adaptation. At the same time, the main challenges for small businesses in modern conditions are: unpredictability of the situation, instability of legislation, lack of personnel, low solvency, lack of capital, etc. The article examines current trends in the development of small business in Ukraine in the first half of the year and the second quarter of 2025, taking into account regional and gender characteristics. An analysis of statistical data on the opening and closing of individual entrepreneurs (IEOs) in various regions of the country, including regions under constant military influence, was conducted. It was found that even in difficult conditions, a number of territories demonstrate positive dynamics of entrepreneurial activity. Thus, in Zaporizhia region, after a significant outflow of business at the beginning of the year, the second quarter recorded a net increase in new entrepreneurs. Similar trends were observed in Kharkiv, Sumy, Mykolaiv and Kherson regions, where the number of new registrations exceeded the number of closures. Particular attention is paid to gender aspects: women continue to be the leading driving force of small business, providing over 60% of new registrations of individual entrepreneurs, while among closed enterprises there is an almost parity distribution by gender. It is concluded that the mass cessation of activities of individual entrepreneurs at the beginning of 2025 was of a temporary technical nature, related to the peculiarities of state registers, and the second quarter witnessed a gradual restoration of economic activity of small businesses. The results obtained allow us to assess the resilience and adaptability of the business environment in the face of crisis challenges and identify key regions and industries to support state policy in the field of small entrepreneurship.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".