Accelerating the transformation to a sustainable food economy by strengthening the sustainable entrepreneurial ecosystem
Bibliographic record
Abstract
Strengthening the sustainable entrepreneurial ecosystem (SEE), particularly its support functions for small to medium-sized enterprises (SMEs), is increasingly seen as an important means of accelerating the transformation to a sustainable economy. Little is known, however, about how to strengthen SEEs. In this article, we evaluate a series of 16 projects intended to develop SEE functioning to accelerate transformation to a sustainable food economy in the Greater Phoenix Area of Arizona. We use an evaluative framework designed around a set of ten SEE support functions to qualitatively assess the baseline state of the SEE, how projects were executed, the effects of these projects, and the overall changes in the SEE that resulted. The findings indicate all but one projects had positive effects on the SEE (nine weak, six medium). In conjunction with other developments, the projects raised the overall SEE performance from the baseline state of two functions being performed at only minimal level, to six functions being performed minimally, and one at a medium level. Insights gained from comparing results across projects suggest tentative guidelines for future practice, which should be useful for SEE stakeholders, including policy makers, economic development agencies, financial institutions, consultants, and educators, interested in strengthening SEEs. Researchers engaging in studies on strengthening SEEs may benefit from the evaluative framework enabling larger cross-case comparisons.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".