The Emotionality of Social Enterprises: Mechanisms and Challenges for Generating Emotional Energy
Bibliographic record
Abstract
Despite the clear importance of emotions for social enterprises, theory on social entrepreneurship still lacks a framework for understanding why emotions are so important for these organizations. Further, theory on social entrepreneurship lacks a framework for understanding the challenges faced by social entrepreneurs in attempting to mobilize their internal and external audiences from an emotion-based perspective. Building on the sociological work that has been carried out on emotions, I provide a framework for understanding how a collective emotionality is manifested in and by social enterprises. I suggest that social enterprises leverage three mechanisms for generating emotional energy: (1) pursuing meaningful ideals, (2) nourishing a vibrant emotional culture, and (3) granting power and status to individuals with less power and lower status. By analyzing these factors, I reveal the challenges that social enterprises face in scaling up, challenges that are relatively unknown compared with the well-explored challenges of balancing social and financial objectives. As social enterprises attempt to scale up their objectives, organizational growth exerts pressures on these efforts, putting these organizations at risk of emotional dispersion.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".