Economic transformation and the institutional environment for entrepreneurship in times of change, using Ukraine as an example
Bibliographic record
Abstract
The article investigates the formation of Ukraine’s business climate during the transition period triggered by the full-scale military invasion of 2022 and explores factors influencing entrepreneurial adaptation to new economic and security challenges. The study underscores the need for a scientific understanding of transformation processes in the business environment, which is affected by military actions, economic instability, inflation, and devaluation, and highlights the role of state policy in supporting businesses during this period. The aim of the research is to comprehensively assess the dynamics of Ukraine’s business climate from 2012 to 2023, identify key factors shaping it, and determine future prospects for entrepreneurial development. Methodologically, the study utilizes horizontal and vertical economic analysis, comparative methods, and statistical data from 2020–2024. Indicators such as the Ukrainian Business Index (UBI) and diffusion index (DI) were employed to measure activity, alongside fundamental and technical analysis techniques. The results show a significant drop-in business activity in 2022 (UBI fell to 29.82), followed by a recovery in 2023 (UBI rose to 38.92), reflecting adaptability under crisis conditions. Small and medium enterprises, particularly in pharmaceuticals, agriculture, and telecommunications, demonstrated resilience, and a 5% GDP growth in 2023 was supported by stabilization in the energy sector and international aid. Future research should further explore the effects of digitalization, deregulation, and financial assistance on the business climate and develop models to forecast economic activity in conditions of uncertainty.
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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 | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| 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 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".