Analyzing external environment factors affecting social enterprise development
Bibliographic record
Abstract
Purpose The purpose of this paper is to analyze how factors in the external environment affect social enterprise (SE) development in Canada. With the decline in government funding for non-profit organizations, SE development is gaining greater traction. SEs are businesses and can be analyzed with methods similar to those for traditional businesses. Just as the external environment is important for assessing the success of businesses, in this study, the authors examine the external environment related to SEs. Design/methodology/approach In this statistical analysis, the authors compared 62 factors across 33 census metropolitan areas (CMAs) in Canada while treating SE revenue as the dependent variable. Links between the dependent variable and the external environment were analyzed through correlation and regression tests. Publicly available revenue figures for non-profit SEs by CMAs were compared with a selection of external environment factors, including demographic information and health indicators, also organized by CMA, as published by Statistics Canada. Findings The analysis demonstrated that three of the factors displayed significant positive correlation and one resulted in a predictive value. Positive correlations were discovered between SE revenue per capita and three of the variables: university education, perceived health, very good or excellent and no religious affiliation. Only university education was found to have predictive value. Originality/value This study is the first to compare SE revenue and the external environment across Canada’s CMAs. The results show that factors in the external environment create conditions more conducive to SE development.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".