The Unintended Consequences of Supportive Institutional Infrastructure on Social Ventures
Bibliographic record
Abstract
Social ventures—newly founded organizations that blend social welfare and commercial logics to deliver innovation for societal change—while becoming an increasingly popular choice of organizational form for addressing complex social problems, often leave social entrepreneurs faced with tensions and trade-offs stemming from multiple and oftentimes contradictory social and business logics. Although tension is intrinsic to the hybridity of social ventures, it can also be unintendedly induced by the context in which social entrepreneurial activities occur. Extending current understanding of the complex role of institutions in both facilitating and hindering the effectiveness of social ventures, our research identifies conditions under which institutional infrastructures meant to coordinate action around a given social problem, unintendedly threaten the effectiveness of social ventures thriving to contribute to solving the problem. Our proposed theoretical framework suggests that, depending on the level of centralization and multiplicity of actors in the institutional infrastructure, social ventures face legitimating and organizing tensions that are likely to threaten their ability to address and manage social-business tensions and, thus, their ability to produce intended results. This enhanced understanding of institutional configurations and related unintended consequences allows us to provide a number of suggestions regarding institutional correction mechanisms targeted at mitigating the likelihood of causing unexpected drawbacks.
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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 | 0.011 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".