Inclusive entrepreneurship in Ukraine: challenges of creation and prospects of development taking into account international experience
Bibliographic record
Abstract
The paper examines modern mechanisms for the formation of inclusive entrepreneurship as a tool for the socio-economic integration of vulnerable groups of the population. The mechanisms for supporting inclusive initiatives in entrepreneurship in Ukraine are analyzed, and a comparative review of the experience of countries such as Canada, the United Kingdom and Germany is conducted. Inclusive entrepreneurship is considered not only as a social phenomenon, but also as an economic strategy capable of ensuring sustainable development and innovation in the business environment. The legislative, institutional, educational and financial mechanisms for supporting inclusive initiatives in Ukraine are analyzed, including microcredit programs, tax breaks and state grant programs. Special attention is paid to the role of inclusive entrepreneurship in ensuring sustainable development, reducing social inequality and promoting employment of persons with disabilities, women, veterans and other socially vulnerable categories. The emphasis is on institutions supporting social entrepreneurship, in particular accelerators of social initiatives, specialized educational institutions (schools of social entrepreneurship), business incubators, as well as developed networks of mentors and experts who provide social entrepreneurs with knowledge, consultations, practical support and guidance at all stages of implementing business ideas. The main obstacles to the implementation of inclusive entrepreneurship in Ukraine are identified: legal uncertainty, insufficient access to financing, lack of mentoring programs, stereotypes in society. The problems are outlined and models of inclusive entrepreneurship development in different countries are analyzed, which made it possible to determine the main aspects of the effective implementation of inclusive entrepreneurship in Ukraine. Comprehensive recommendations are proposed for improving state policy in this area, expanding partnerships between state institutions, public organizations and the private sector, as well as implementing a national strategy for the development of inclusive entrepreneurship, taking into account best international practices.
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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 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.005 |
| 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".