SOCIAL ENTREPRENEURSHIP AND LEARNING: THE CASE OF THE CENTRAL AMERICA LEARNING ALLIANCE
Bibliographic record
Abstract
This paper sets out to analyze social entrepreneurship in the Central America Learning Alliance, in the context of recent literature on entrepreneurship and learning. Drawing on a recent and rapidly growing literature that describes entrepreneurship as a process that is inherently dynamic and experimental, with learning as a core component, we focus on social entrepreneurship in development as a catalyst of social transformation. A case study of a multi-stakeholder network focused on promoting processes of rural enterprise development, known as the Central America Learning Alliance, is used to illustrate social entrepreneurship in the context of the framework for innovation and learning that is developed in the first part of the paper. We conclude that the key concepts underlying entrepreneurial learning have important implications for social entrepreneurship in the context of building dynamic livelihoods for the poor of Central America.
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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 | 0.002 |
| Science and technology studies | 0.026 | 0.018 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.004 |
| 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".