The potentials and perils of prosocial power: Transnational social entrepreneurship dynamics in vulnerable places
Bibliographic record
Abstract
Social entrepreneurs can be powerful change agents for alleviating the suffering of the disadvantaged. However, their prosocial motivation and behavior frequently result in detrimental impacts on those they intend to support, especially when their operations span different socio-spatial contexts. We conducted a multiple comparative case study among 12 transnational social entrepreneurs of foreign, domestic non-indigenous, and local indigenous origin, who are seeking to improve the livelihoods of indigenous communities in rural Ecuador. We introduce the concept of prosocial power to social entrepreneurship research and demonstrate how it can work as a double-edged sword in the hands of transnationally embedded social entrepreneurs who operate in vulnerable places. Context-bound variations in social distance, bi-directional learning, reflexive impact measurement, and socio-spatial dominance were identified as being decisive for prosocial power to lead to positive or negative impacts on disadvantaged others.
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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 | 0.002 | 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 | 0.007 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".