How to Grow Successful Social Entrepreneurship Firms? Key Ideas from Complexity Theory
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Social entrepreneurship (SE) is increasingly popular in academia and practice, but unified theoretical explanations about the performance of social entrepreneurship firms (SEFs) is missing (Santos, 2012). This deficiency motivates us to theorize about SE from a complexity science perspective. We draw from complexity science to analyze and explain how SEFs emerge, achieve performance, and grow. We link complexity science with SE so as to add explanatory value as well as offering guidelines for better SEF performance toward achieving social objectives while avoiding the chasm of chaos. Our theoretical framework offers complexity insights for building effective networks, and accountability, as well as for improving trust, legitimacy, and sound governance. Drawing on complexity theory to better explain the key elements necessary for improving SEFs’ performance and growth, enhances the probability of meeting the challenge of the so-called ‘double bottom-line’: achieving continuous positive social impacts while attaining financial health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it