TECHNOLOGY ENTREPRENEURSHIP @ SFU: EXPERIENTIAL, INTERDISCIPLINARY LEARNING THROUGH AN IMMERSIVE TWO-YEAR PROGRAM
Bibliographic record
Abstract
This paper provides an overview of experiences to date in developing and delivering the Technology Entrepreneurship @ SFU program - an interdisciplinary program in which Mechatronic Systems Engineering and Business students at Simon Fraser University collaborate on market-driven, entrepreneurial initiatives that simultaneously satisfy their respectivecapstone project requirements. The paper includes a discussion of program scope and objectives, design, implementation and revisions. The role of the program in providing experiential, interdisciplinary learning for Engineering and Business students through an immersive experience is discussed. Qualitative and quantitative program results to date are reviewed, with program enrollment and completion trends, course evaluation data and key stakeholder metrics indicating a level of successin: (1) creating an interdisciplinary, entrepreneurial culture amongst the students, (2) preparing students for future career paths that may include entrepreneurship and (3) potential further commercialization of prototypesdeveloped in the Technology Entrepreneurship program. Longer-term measures of success are also considered and discussed in light of the experiences to date.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".