The Political Side of Social Enterprises: A Phenomenon‐Based Study of Sociocultural and Policy Advocacy
Bibliographic record
Abstract
Abstract This study explores the often‐overlooked political dimension of social enterprises, particularly their advocacy activities aimed at influencing public policy, legislation, norms, attitudes, and behaviour. While traditional management research has focused on commercial activity and the beneficiary‐oriented aspects of social enterprises, this paper considers their upstream political activity. Using a phenomenon‐based approach, we analyse original survey data from 718 social enterprises across seven countries and six problem domains to identify factors associated with their engagement in advocacy. Our findings reveal that public spending and competition in social enterprises’ problem domains, as well as their governance choices – legal form, sources of income, and collaborations – are significantly associated with advocacy activities. We propose a new theoretical framework to understand these dynamics, positioning social enterprises as key players in markets for public purpose. This research underscores the importance of recognizing the political activities of social enterprises and offers new insights for studying hybrid organizing and organizations that address complex societal challenges. By highlighting the integral role of advocacy, our study contributes to a more comprehensive understanding of how social enterprises drive social change, not only through direct service provision but also by shaping the broader sociopolitical environment.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".