The Spirit of Social Entrepreneurship and Institutional Environment as Drives of Sustainable Economic Growth
Bibliographic record
Abstract
The impact of the COVID-19 pandemic is widely spread, not only multi-sectoral, but also across many fields, especially in socio-economic life and institutions of the communities.The purpose of this paper is to explain the importance of building a spirit of social entrepreneurship and the institutional environment to accelerate economic recovery and growth caused by the impact of the COVID-19 pandemic.The research method used a mixed methods research design.Qualitative and quantitative descriptive analysis was used through exploratory and explanatory designs.The main data source is primary data obtained through a survey of social entrepreneurship actors in various regions in Indonesia.The main contribution is to provide a conceptual model that integrates the spirit of social entrepreneurship (SSE) into new institutional economic theory (NIE).The results of the study concluded that the role of social entrepreneurship (SE) and the institutional environment has not been optimized to support economic growth.Hence, there is a need for a spirit and existing institutional environment quality to encourage sustainable economic growth.Without a strong spirit and encouragement from the quality of the institutional environment which has political authority, the SE will be difficult to become a formal and strong entrepreneurial and cultural economic movement.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 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".