Instilling innovation and entrepreneurship in engineering graduate students: Observations at the University of Calgary
Bibliographic record
Abstract
Abstract Today's employers not only want graduates who are critical thinkers and problem solvers that are able to work in teams, but also individuals that understand innovation and how to use entrepreneurial activities to move innovations to become benefits to society. For research‐based graduate students, this is even more desired, with emphasis on an understanding of innovation processes and the realization of the role that innovation plays for the survival and growth of existing corporations as well as the key contribution it makes in start‐up companies. Many engineering programs focus on traditional engineering attributes, and although these are essential elements that engineering graduates should learn through their training, little attention is paid to innovation and the conversion of innovations to realized impacts. Here, we review innovation and discuss our experience with trainees (graduate students and post‐doctoral scholars) and how to engage them in innovation activities. Our observation is that innovation is coachable and can be cultivated in research‐based trainees. We recommend nine actions (understanding the challenge, motivation, safe and mentored local environment, tolerance to failure, diversity, rewarding of passion, awareness of the external, internal‐external environment, and creative destruction and preservation) that trainees should be exposed to in order to promote innovation and entrepreneurial activities so that they can establish and/or strengthen their innovation and entrepreneurship muscles, which hopefully continues after they leave university.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".