Green community entrepreneurship: creative destruction in the social economy
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the process of green community entrepreneurship in the social economy by studying creative responses among environmental non‐profit organizations to an external fiscal shock. Design/methodology/approach A total of 12 managers of environmental non‐profit organizations were interviewed to identify and classify their responses to a single external fiscal shock. These organizations are connected by a social capital network, their national association, Green Communities Canada. The social economy and ecological economics literatures are reviewed to construct a definition of green community entrepreneurship. Interview respondents identified factors which facilitate this process. Findings The need for green community entrepreneurship was driven by two interrelated issues (a loss of external government funding, and an associated market collapse for residential energy audits), and facilitated by three main factors (external social capital network flows, internal human capital stocks, and strategic partnerships). Research limitations/implications Future research should examine other social economy organizations to determine if the dynamics discovered here are unique to green community organizations delivering climate change programs or apply more generally. Policy implications include the potential for joint project creation and investment utilizing green community entrepreneurship to integrate social and ecological economy objectives. Originality/value A new conceptual framework for green community entrepreneurship is developed.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".