Seizing the moment—Strategy, social entrepreneurship, and the pursuit of impact
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.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".