Academic Entrepreneurial Engagement among Academics in Canada and China: The Impact of Research Orientation and University Expectations
Bibliographic record
Abstract
Despite a growing awareness of academic entrepreneurship undertaken by professors around the globe, there remain unanswered questions regarding how individual and organizational characteristics shape academics’ decision to engage in entrepreneurial activities. Drawing on data from the 2017–18 Academic Profession in the Knowledge-based Society (APIKS) survey, this study examines research-based and teaching-based academic entrepreneurship engagement in two countries, namely Canada and China, and examines through logistic regressions how academics’ individual research orientation and perceptions of their university’s expectations affect their likelihood of engaging in entrepreneurial activities. The results show that a majority of faculty members in the two countries are involved in entrepreneurial activities, including research-based activities (such as contract research, joint research and publications, and consultancy) and teaching-based activities (such as supervising student internships, volunteer-based work, and public lectures). Regression results suggest that academics who emphasize a theoretical research orientation are less likely to demonstrate entrepreneurial involvement, while academics who report a practical, commercial, or social research orientation are more likely to demonstrate entrepreneurial involvement. Academics who perceive that their university expects them to engage in entrepreneurial activities are also more likely to do so. These findings shed light on ways to reinforce academics’ social involvements and contributions in both countries.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".