From Balancing Missions to Mission Drift: The Role of the Institutional Context, Spaces, and Compartmentalization in the Scaling of Social Enterprises
Bibliographic record
Abstract
In this article, we explain the mechanisms that allow social enterprises to balance their missions, and the risk of mission drift as organizations grow. We empirically explore Incubator-BUS (I-BUS), a student organization within a private Brazilian university, which sought to incubate cooperatives for vulnerable groups. Although initially successful in balancing its missions, I-BUS then failed. We show how scaling-up can complicate the balancing of different missions within the same organization. We propose that, to balance their missions, social enterprises—especially recently formed and democratically managed enterprises—need not only “spaces of negotiation,” as suggested in the literature, but also “herding spaces” that connect an organization to its institutional context. We indicate why herding spaces are critical, but then show how scaling-up can result in organizational “compartmentalization” that undermines them.
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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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.033 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.002 | 0.003 |
| 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".