Turning rebellion into money? Social entrepreneurship as the strategic performance of systems change
Bibliographic record
Abstract
Abstract Research Summary Critical scholars recognize a disjuncture between the problems identified by social entrepreneurs and the solutions they propose. Existing theory treats this as a problem to be rectified at the organizational level. In this essay, we widen attention to the macro‐oriented systems change strategies of social entrepreneurs. We develop a dynamic typology showing how strategies are reassembled over time to stimulate or deflect desire for systems change. Deriving inspiration from Goffman, we theorize the ways that different types of systems change actor perform systems change via interaction with their environments. Drawing on illustrative cases on the boundaries of social entrepreneurship, we show how the collective action frameworks developed by systems change actors can be adapted and repurposed by their (systems) audiences: effectively turning rebellion into money. Managerial Summary Social entrepreneurs often call for systems change to tackle wicked problems such as poverty or climate change. However, the strategies they propose for tackling these problems, such as lending money to poor people are considerably less radical. In this essay, we identify three types of systems change actor distinguished by the degree of systems change they call for. We trace their ideas over time to illustrate how strategies are mediated, and subsequently repurposed through interaction with the systems they seek to change. In conclusion, we call upon researchers and social entrepreneurs to widen their perspectives to incorporate more radical ideas and potentials for systems change, and for greater attention to be devoted to scrutinizing and protecting the integrity of systems change strategies.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.055 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".