Profiles of entrepreneurship students: implications for policy and practice
Bibliographic record
Abstract
Purpose While much research seeks to determine the impacts of entrepreneurship education on students, far less attention has been paid to students’ motivations and interests. Understanding students’ perspectives is useful, particularly as governments support the expansion of campus entrepreneurship. The purpose of this paper is to develop a deeper understanding of students’ reasoning in relation to pursuing entrepreneurship education. Design/methodology/approach Specifically, the key questions driving this study were: Why do students join experiential learning entrepreneurship programs? How do they define their goals for entrepreneurship education, and what outcomes do they value? Data were collected through interviews with 38 students participating in a range of experiential entrepreneurship programs in Ontario. Findings Four different patterns in students’ reasoning and sense making emerged from the analysis. First, “venture creators” are the prototypical student entrepreneurs who are set on creating and launching a venture. Second, “experience seekers” aim at gaining practical work experience but do not see themselves as nor intend to become entrepreneurs. Third, “explorers” aim at developing familiarity with basic concepts and opportunities in entrepreneurship, as a means to consider whether this is an attractive career option. Finally, “engagers” are actively experimenting with entrepreneurship as they gauge their “fit” and potential as entrepreneurs. Originality/value This study’s findings provide an empirically grounded check on the assumptions guiding government policy for entrepreneurship education and institutional practice. Policy and institutional attention is overly focused on venture creation, even as other outcomes are commonly espoused. Recognizing different profiles of entrepreneurship students may lead to more purposefully designed programs that have different objectives, and help distinct segments of students achieve their goals.
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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 | 0.012 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".