Bibliographic record
Abstract
Purpose The purpose of this paper is to identify important elements of the evaluation and definition of success in social entrepreneurship. It considers previous approaches and the lessons that can be learned from other fields of organizational studies. Design/methodology/approach The method used is based upon an objective and subjective, social constructionist view of organizational success. The paper reviews the fields of strategy, organization theory, entrepreneurship and innovation to identify relevant frameworks, measures, definitions of success, and the implications of the choice of success measures on our understanding of various phenomena. Findings From this perspective, it becomes apparent that how success and failure are defined is based on assumptions about the value of social enterprise and the nature of social change. In order to develop a deeper understanding of the drivers of social enterprise, there must be experimentation with a rich complement of success measures that are not limited to the triple bottom line. Practical implications The paper is of use to social enterprise researchers, practitioners and consultants who are defining what it means for a social enterprise to be successful. The insights should allow for a more conscious evaluation of a range of potential success measures and the impacts they have on our social outcomes. Originality/value Although measuring social enterprise success is recognized to be an important topic, most work in the field implicitly or explicitly identifies success based on a goal‐centred evaluation of the triple bottom line. The paper challenges this thinking to include subjectivity, causation, contestation, organizational form and the multiple polar dimensions that must be balanced by every organization. It draws on research from related fields that have already struggled with these issues and can offer valuable lessons for social enterprise.
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.
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 | 0.039 | 0.145 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".