Do entrepreneurship programs matter? An analysis of North American university innovation systems
Bibliographic record
Abstract
Are there differences in commercialization outcomes between universities in Canada and the USA? If so, why? Can possible divergences be explained through a greater movement of some universities towards entrepreneurial culture? We measure the presence and growth in numbers of entrepreneurship centers to determine if there are any parallels or discernable patterns that may be related to spinout performance. We then examine the commercialization performance of universities on both sides of the 49th parallel through a key indicator: university spinouts generated. Based upon theories that suggest entrepreneurial culture plays a significant role in the spinout process, we then test the hypothesis that entrepreneurship education programs play a significant role in determining spinout performance. Our model assumes that the level and intensity of an academic entrepreneurship program/center is a valid indicator of 'entrepreneurial culture' that may impact upon a universities propensity to spinout new knowledge intensive firms. Our results find that there is indeed a correlation between intensity of entrepreneurship program and commercialization outcomes. It also provides empirical evidence that supports the theory that innovation is dependent upon both knowledge and entrepreneurial capacity within these systems.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".