From Science to Policy: How to Support Social Entrepreneurship in Croatia
Bibliographic record
Abstract
Entrepreneurs are constantly looking for new models to address growing global challenges in a sustainable manner. Over the past several decades, those challenges have been identified and responded to through the development of social entrepreneurship. There is a number of research dealing with the theoretical concepts of those topics; however, the definitions and framework for action are different from country to country. Having in mind that the main idea of social entrepreneurship is to enable decent work for employees and to gain broader welfare for communities, the purpose of this paper is to analyse the development of social entrepreneurship in Croatia. The research is focused on recent developments, connecting key definitions and principles of social entrepreneurship with common trends and concrete case studies. This study’s results show that there are different approaches to social entrepreneurship globally. However, social entrepreneurship in Croatia develops within a clear legal framework. The current state of social enterprises is connected with respective public policies, while the number and types of social entrepreneurs are constantly rising in the last few years. The results of the analysis also show that there are still actions to be taken in order to encourage future policy measures aiming to support social entrepreneurs in Croatia.
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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 | 0.020 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".