Seizing the moment—Strategy, social entrepreneurship, and the pursuit of impact
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
Abstract Research Summary Social entrepreneurship continues to grow as an impactful phenomenon in the world and as a rich stream of research. Given this exciting growth, there is value in proactively exploring how social entrepreneurship scholarship can thrive and “seize the moment” as it matures. This special issue solicited papers at the intersection of strategy and social entrepreneurship in hopes of providing a road map for future scholarship. This editorial introduces and integrates the special issue paper contributions across three emergent themes: (1) diverse actor characteristics, (2) competing environmental factors, and (3) heterogeneous outcomes. We organize a research agenda that extends from the special issue, which we hope will motivate a new wave of research that derives benefits from the integration of strategy and social entrepreneurship scholarship. Managerial Summary Social entrepreneurship is increasingly common as business leaders seek to integrate social and/or environmental objectives into its economic activities. With this growth comes the need to examine how social entrepreneurs strategically manage the intertwining of social and economic activities. The papers in this special issue make progress in this regard, examining how differences in actors involved in social entrepreneurship and the environments in which they operate shape social/economic outcomes. The papers in this special issue make important progress in bringing strategy concepts to bear, and lay the groundwork for future research that helps better explain how, why, and to what degree social entrepreneurs have a positive impact. This special issue thus offers insights for researchers, policymakers, educators, and entrepreneurs about how to sustain impactful social/environmental activities over time.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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