The Role of Education in Entrepreneurship: Two Canadian Stories
Bibliographic record
Abstract
The role that education and training plays in entrepreneurial success is one that is subject to lively debates in the literature. In the first half of this paper, we explore the essence of this debate, with a particular focus on women entrepreneurs in Canada. In the second half of the paper, we tell the stories of these two Canadian entrepreneurs who have started technology-based businesses and were participants in a unique Canadian non-profit educational enterprise, Shad Valley Centre for Creative Technology. The stories of these two successful entrepreneurs - Thelma Zee, who started a freelance web design business while she was still in high school, and Jennifer Corriero, who started a now world-renowned non-profit organization for youth - aim to shed light on the role they see for education in entrepreneurship. Our cases suggest that personal characteristics are important — persistence, self-confidence, initiative, creative thinking, and as Thelma said, a certain kind of intelligence that isn't really about being "book-smart," although it often includes that too. Our research also asked the question, "Is learning important?"Traditional formal education plays a relatively small role in these two cases and the others that we have gathered through our study. Based on our research, we would argue this reflects the relatively low importance of formal education as specific preparation for entrepreneurship for women. In our stories, the informal learning that young people receive from family and friends was important. Furthermore, participation in the Shad Valley program - its intensity, level of challenge, and focus on experiential and applied learning — played a key role in inspiring these women to take on the entrepreneurial initiatives.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.065 | 0.015 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".