Are Universities Playing a Role in Nurturing and Developing High-Technology Entrepreneurs? The Administrators' Perspective
Bibliographic record
Abstract
This is a study of the administrators' perspective on entrepreneurship education: for example, the extent of, and their support for, entrepreneurship course offerings, and formal and informal initiatives for venturing students. Sixty per cent of the 134 deans in the survey offered entrepreneurship courses in their faculty (that is, 100% of business, and 36% of humanities and social science deans). Relatively few deans of medical sciences, science and engineering and graduate studies offered entrepreneurship courses, although many deans in these faculties were supportive of their faculty offering courses. For informal programmes, 66% of those that offered courses also had ‘non-credit’ programmes to support venturing students, but only 48% of those with no courses had these programmes. Findings show that universities in Canada are not optimizing opportunities to nurture high-technology entrepreneurs.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".