Engineering Entrepreneurship Teaching and Practice in the United States and Canada
Bibliographic record
Abstract
Innovation and entrepreneurship have become priority areas in many engineering faculties. However, there is no consensus about how best to incorporate these or what content should be included in courses. The purpose of this article is to explore how entrepreneurship is presented in an engineering context and how this compares with how it is offered in other contexts. Two empirical studies are presented. The first study examines the content of different types of entrepreneurship courses. The second study is an analysis of communications by engineers and entrepreneurs. Results of these studies show that engineers discuss and present entrepreneurship differently, both within engineering programs and in professional communications. Engineers are much more inclined to focus on engineering design, problem solving, product development, and idea generation in entrepreneurship. They are also focused on the details of the innovation and design processes and less on the entrepreneurial process itself. At the same time, there is a gap between academic entrepreneurship programs and discussions by practitioners. In practitioner discussions, the context and entrepreneurial ecosystem are much more important than they are in entrepreneurship courses and the necessity of education for entrepreneurial success is less valued.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.013 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".