Economic transformation and the institutional environment for entrepreneurship in times of change, using Ukraine as an example
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it