Innovation and Entrepreneurship for Social goals and Sustainability in developing countries
Bibliographic record
Abstract
The shift towards a sustainability-driven society includes changes to the educational system, business operations, innovation and entrepreneurial ecosystems as well as policymaking. Moreover, such a shift demands particularly a combination of top-down policy-making initiatives and bottom-up social entrepreneur-driven changes. Social innovation and entrepreneurship are providing solutions for globally recognized social and sustainability challenges such as poverty, education, environmental and climate change, peace support – worldwide yet also in the particularly challenging context of developing economies. We aim to showcase the best practices of social and sustainability-oriented innovation and entrepreneurship in the context of developing economies. In particular, we address the question of how social entrepreneur and innovator with bottom-up ideas could complement the top-down policymaking initiatives. Our design implies qualitative research aiming to disseminate the inspiring story of a social innovative enterprise, which represents a successful example of complementing policy-making efforts. Accordingly, our findings contribute to the literature on social innovation and entrepreneurship in the context of developing economies and simultaneously informs social entrepreneurs and policymakers on potential opportunities for synergy in their efforts.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".