Social Entrepreneurship in the Long Run:Lessons from a 55-Year History of a Fair Trade Organization
Bibliographic record
Abstract
The social entrepreneurship literature largely focuses on internal practices for managing social and commercial goals, implicitly assuming a relatively stable external environment. Considering the struggles of many social enterprises in economic downturns, we investigate how social enterprises manage to survive and grow in the long run, or fail to do so, in changing macroeconomic conditions. Through a qualitative study of a fair trade social enterprise that operated for 55 years (1965–2020) in Canada, we identified two distinct approaches to growth: relation-based and expansion-focused approaches. While expansion-focused approaches enabled the social enterprise to generate greater impact in favorable macroeconomic conditions, they aggravated financial challenges in economic downturns. In contrast, relation-based approaches led to only modest growth yet provided a source of long-term endurance through community-based assets and relationships, even in the aftermath of major economic recessions. By conceptualizing different approaches to the growth of social enterprises and their far-reaching implications, we contribute a novel understanding of the long-term survival and growth of social enterprises in evolving macroeconomic contexts.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.019 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".