From what we know to how we use it: five principles for turning entrepreneurship research and practitioner action guidelines
Bibliographic record
Abstract
This paper introduces a 'third stream' of publication, that will appear in the Journal of Small Business and Entrepreneurship(JSBE), the journal of the Canadian Council for Small Business and Entrepreneurship/Conseil Canadien des PME et de l’entrepreneuriat (CCSBE/CCPME), and in Small Enterprise Research (SER), the journal of the Small Enterprise Association of Australia and New Zealand (SEAANZ). In this stream-founding paper it is argued that entrepreneurship researchers, currently, do not place sufficient emphasis on making their research findings relevant to entrepreneurs and their advisors, educators and those working in government on policy and programs. The paper then presents five general principles for turning entrepreneurship research findings into practical action guidelines for practitioners. The piece ends with a description of a new section to appear in both JSBE and SER beginning with this issue.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".