ENTREPRENEURIAL IDENTITY AND ENTREPRENEURSHIP EDUCATION
Bibliographic record
Abstract
Entrepreneurial identity shapes thoughts and actions of entrepreneurs during the process of opportunity recognition and new-venture creation. Entrepreneurship education as a context is expected to facilitate the emergence of entrepreneurial identity among students. In my thesis, I present three studies that explore the nature of entrepreneurial identity and its impact on career identities. After the first introductory chapter, I examine in the second chapter a Graduate Program of Entrepreneurship and Innovation (GPEI) at the school of engineering and an entrepreneurship stream at MBA program (EnMBA) in two prestigious Canadian universities. I discovered a new unintended career specialization that I identify as an “entrepreneurship profession.” This study contributes to the theory of legitimation by identifying elements that impact and were impacted by the newly emerging entrepreneurship education program. My findings provide insight into the institutionalization of new fields, as well as the evolutionary properties of management education. In the third chapter, I examine the nature and emergence of an entrepreneurial identity among students of an entrepreneurship Bachelor of Commerce program in Toronto, Canada. I found entrepreneurial identity to be a self-perceived meta-identity that enables individuals to reject aspects of their current role identities and create new ones. In the fourth chapter, I examine how individuals who graduated from entrepreneurship programs use their entrepreneurial identities in shaping their careers. I found that entrepreneurial identity acquired during entrepreneurship education shapes the profiles of graduates, and five career paths were identified: dream-building, entrepreneurship pop culture, institutional entrepreneurship, investment entrepreneurship, and new venture path. I argue that entrepreneurship education might not prepare its graduates to become founders, but it empowers them with an entrepreneurial identity awareness and entrepreneurship institutional knowledge. In the fifth chapter, I conclude by discussing the impact of this research on my academic and personal identities. I elaborate on future research opportunities and my research program. I also reflected on my own entrepreneurial identity and its impact of my academic career.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".