CLASSIFICATION AND EVALUATION OF SOCIAL ENTREPRENEURSHIP DEVELOPMENT INDICATORS
Bibliographic record
Abstract
The purpose of the study is to classify and evaluate indicators of social enterprise development for countries with the most favourable conditions for their functioning. Methodology. The study uses indices as an assessment tool. The method of grouping indicators was used, which allowed to identify two components of social entrepreneurship development: economic and social. The basis of the analysis is the use of additive, multiplicative and additive-multiplicative models, which allows comparing the results and determining the most effective model for a particular country. To evaluate the development of social entrepreneurship, the Thomson Reuters Foundation report "The best countries to be a social entrepreneur" was used. Results. Studies have shown that the highest value of the social enterprise development index is achieved when using different models depending on the country chosen, i.e., if the highest level is achieved when using an additive model (Singapore, Denmark, Chile), this means that the low level of development of one component is compensated for by a higher level of other components. If the highest value is achieved when using a multiplier model (Canada, Australia, France, Belgium, the Netherlands, Finland, Indonesia), then it is important for the country to take into account all development components simultaneously. The additive-multiplicative model allows countries to vary the components and determine how they want to move forward to achieve the highest level of social entrepreneurship development. Practical implications. The classification and evaluation of indicators for countries allows to identify "stimulators" and "disincentives" for the development of a social enterprise, as well as to determine the nature of their impact: economic (through material incentives), non-economic (social). This allows each country to develop its own algorithm for implementing such an innovative form of business to achieve maximum effect, i.e., to solve socio-economic problems and increase the level of development in the future. Value/originality. In the context of escalating conflicts at both the global and local levels, the number and complexity of socio-economic problems are increasing, and they need to be addressed through the use of creative and innovative methods, as traditional mechanisms have failed. That is why social enterprises are an effective form of business that will allow not only quantitatively but also qualitatively to ensure the achievement of this mission. This research focuses on the factors that influence the development of social enterprises and can be used by countries to formulate public policies to support this innovative form of business.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".