Assessing the current state of university-based business incubators in Canada
Bibliographic record
Abstract
This paper explores the current state of university-based business incubators (UBIs) in Canada by utilizing both secondary and primary data obtained through desk-based secondary research and semi-structured interviews with UBI managers, academics, and support staff. These data informed the development of nine cases of UBIs in Canada. The data were collected from VoIP (Voice-Over-Internet-Protocol) based semi-structured interviews with 32 participants during the COVID-19 pandemic (March 2021–February 2022), from which 9 cases were developed during the pandemic. The key themes derived from the findings were the development of communication skills, curriculum development, extra-curricular activities, industry engagement, innovation, research skills and strategic thinking. The originality of this study lies in its identification of the current state of UBI activities as well as its assessment of the broad range of activities and provisions among Canadian UBIs. The empirical development of showcasing these initiatives is also novel for the efficacy of UBIs concerning institutional and managerial decision-making and operational planning. There are implications for academics, senior management in higher education, entrepreneurs, policymakers and other stakeholders in the entrepreneurship ecosystem.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".