Entrepreneurship Education and Engineering Students
Bibliographic record
Abstract
A 15-year cohort of graduates of an engineering degree programme at a major Canadian university who had taken either one (1EES) or three (3EES) elective entrepreneurship course(s) and a randomly stratified comparison group are the subjects of this paper. Career path, business start-ups, ownership, performance and satisfaction with their entrepreneurship education are examined. Being male and taking one or more courses in entrepreneurship proved to be a strong predictor of business ownership. Significantly more of the 1EES group had started businesses (48% had owned a business at some time since graduation) than those in the comparison group (26% had owned a business at some time since graduation). However, business performance was not significantly different according to group. Taking one or more courses in entrepreneurship was also a strong predictor of later reaching top management status. Significantly more of the 1EES group, who were not business owners, were employed in top management positions. This study also provides information on the time lag from graduation to venturing, on business characteristics, and on the desire for an entrepreneurial career in the future. Findings are important for educators and policy makers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".