How Canadian Universities Align Their Curricular and Co-curricular Programs with Institutional Culture and Entrepreneurial Ambitions
Bibliographic record
Abstract
The Canadian economy currently ranks as the 9th largest in the world in terms of GDP.As technology-driven entrepreneurship becomes increasingly important for creating jobs and wealth and for gaining a competitive advantage on a national level, both engineering students and innovative employers are demanding more exposure to and training in innovation and entrepreneurship.Universities must respond to these growing demands in creative ways, but there is currently a lack of standardization in the design and delivery of entrepreneurship education programs, making it difficult for educators and public funders to compare programs across institutions.Furthermore, each school has a unique institutional culture and entrepreneurial ambition that may shape its definition of entrepreneurship education and its approach to program design.Some programs may focus on design, others on sustainability, and still others on the scalability of firms.To address these challenges, the authors of this work propose a framework for aligning institutional culture and entrepreneurial ambitions with program design.The process of constructive alignment will provide a better understanding of the current practices in engineering entrepreneurship education and bring clarity to the diverse approaches used in pedagogy.By developing a standardized framework, educators and funders will be better equipped to evaluate and compare different programs, ultimately leading to improved outcomes for both students and educators.
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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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.015 | 0.003 |
| Scholarly communication | 0.015 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".