Analysis of social entrepreneurship ecosystems in the international context
Bibliographic record
Abstract
The article examines the ecosystem of social entrepreneurship, its players and identifies its types. The topics of ecosystem and ecosystems of social entrepreneurship are studied in various scientific and literary sources. It has been established, the ecosystem of social entrepreneurship has rarely been the subject of ecosystem studies. Based on the analysis of ecosystems in the international context, the main problems of the social entrepreneurship ecosystem in Ukraine have been identified. The social entrepreneurship ecosystem is a comprehensive tool for supporting social enterprises and social initiatives that aim to solve social problems and improve the quality of people’s lives. The ecosystem formula is a triangle that combines local government, community, and business into a coherent network to support and promote the growth of social enterprises. Ecosystem logic allows us to maintain the required level of innovation and not lose our position in the competitive market. Depending on the key players and elements, the social entrepreneurship ecosystem can be typed through certain components. 7 different types of social entrepreneurship ecosystem and their brief characteristics are presented in this article. In the study, we analysed ecosystems in four countries, including Ukraine. It is found that in the studied countries there is a significant number of types of social entrepreneurship ecosystems. Most social entrepreneurship ecosystems are present in the Netherlands — six types, of which the business ecosystem is the most powerful and efficient. Canada has the most developed ecosystems, and the financial ecosystem is a priority. There are five social entrepreneurship ecosystems in Malaysia, the same as Canada, but the priority is given to the state ecosystem. So, from the experience of these countries, to develop a powerful and effective ecosystem of social entrepreneurship in Ukraine, we must take the experience of government support from Malaysia, experience in financial initiatives from Canada and experience in creating collaborations between business, private sector, and social entrepreneurs from the Netherlands. The leaders in the development of the social entrepreneurship ecosystem in Ukraine are Ternopol and Vinnitsa, two regional centres. Obstacles to social entrepreneurship in Ukraine are lack of funding, low level of social awareness, lack of understanding on the part of society, imperfect legal regulation, and insufficient government support. It is determined that for the effective operation of the social entrepreneurship ecosystem in different countries of the world, there should be assistance from the State, business, and the public, as well as improving the conditions for their activities.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".