Creating social entrepreneurship for rural livelihoods in Bangladesh: perspectives on knowledge and learning processes
Bibliographic record
Abstract
Social entrepreneurship is regarded as a way to ameliorate the situation of the poorest in developing countries, namely those at the Bottom of the Pyramid (BoP). BoP entrepreneurs operate in a severely resource-constrained environment, ‘making do’ with the resources at hand in a process called bricolage . This is essentially a learning process, acquiring and transforming knowledge to come up with new, improved solutions to improve both the entrepreneur’s and the community’s situation. We studied how learning processes develop over time and how a non-governmental organisation, PRIDE, could stimulate entrepreneurial learning among poor women in a rural areas of Bangladesh. We gathered data during two years of monitoring, group interviews and individual interviews with entrepreneurs, their families and people from their networks. Our findings suggest that both formal training and learning from conducting experiments are effective, mutually reinforcing mechanisms. Initially entrepreneurs mainly experience single loop learning in training settings. The first double loop learning event occurred after they saw the positive results of their own successful experiments newly acquired knowledge and occurred in the affective dimension, when they realize they can be entrepreneurs. Social entrepreneurial development could well be combined with institutionalising joint learning processes in a larger entrepreneurial network, gradually leading to joint learning sessions and the co-exploitation of entrepreneurial opportunities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.000 | 0.004 |
| 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".