Bibliographic record
Abstract
Purpose – The purpose of this paper is to investigate a second round of intensive green community entrepreneurship, a form of social entrepreneurship, by a set of environmental service organizations (ESOs) facing the loss of their largest revenue source (the ecoENERGY program), to see if it differed from responses to a similar funding cut five years earlier. In particular, the study compared green community entrepreneurship rates and types to those of the previous program (EnerGuide for Houses) cancellation and examined the perceived importance of various factors, including a social entrepreneurship training program offered by the national association. Design/methodology/approach – Interviews were held with executive directors who had led their organization through both periods of financial crisis. Information was collected on changes in revenue, staffing, residential energy evaluations conducted, service creation, and the perceived importance of organizational factors. The adaptation strategy undertaken by each ESO was classified as resilience, transition, or transformation focussed. Findings – First, green community entrepreneurship is accelerated when needs are heightened, such as when ESOs face funding cuts. Second, only some of the new services or activities launched were financially successful and remained viable over a five-year period. Third, green community entrepreneurship is an important initiative for ESOs to implement their adaptation strategy (resilient, transition, or transformation strategy). Fourth, a higher perceived difficulty of adaptation to funding cuts is associated with the launch of more new services by the ESO. Originality/value – The original contributions of the paper include the verification of repeated increases to the rate of entrepreneurship undertaken in response to sudden funding cuts, as compared to the rate of entrepreneurship during a stable funding period. This accelerated creation of new services can be directed to achieve various adaptation strategies from creating new services in the established area of energy expertise, to initiatives in new areas of sustainability services, such as water, food, or finance. The importance of collective innovation is highlighted with the use of both local and national networks.
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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.080 | 0.048 |
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".