Mapping the Pathways of Science-Based Academic Entrepreneurs in the Queen's University Ecosystem
Bibliographic record
Abstract
Societies are looking to universities to solve global problems and provide economic opportunity through the transfer of research informed knowledge to impact. Previous research has shown that Canada invests significantly in university-anchored research but underperforms in business-related research (Hinton, et al., 2023). This study aims to identify significant factors and pathways influencing academic entrepreneurs in Science, Technology, Engineering, and Mathematics (STEM) and Health Science fields in the Queen’s University non-metropolitan ecosystem. Pathways in this study are analyzed through themes and associated factors rather than as a fixed sequence of events or a strictly temporal progression. The Queen’s University ecosystem, situated in a mid-sized town and anchored by a comprehensive medical-doctoral university, serves as an example of a self-contained environment. University-based research commercialization has become crucial for economic growth and addressing global challenges (Amry et al., 2021). However, there remains a gap in understanding the specific factors and pathways shaping science-based academic entrepreneurship, especially in non-metropolitan settings like Queen’s University, within a multi-level, multi-method framework. The research utilized a mixed-methods approach, beginning with process-oriented case study interviews to gather contextual insights and develop a relational visual model of academic entrepreneurial journeys. This was complemented by broader, structural quantitative survey data, which supported Exploratory Data Analysis (EDA) and Qualitative Comparative Analysis (QCA) to explore the frameworks influencing the Queen’s University innovation ecosystem. Modern data visualization tools were used to cluster variables and create visual and conceptual models, with a strong emphasis on pathway mapping to capture the complexity of entrepreneurial trajectories beyond traditional metrics. Key findings indicate that prior experience with patents, startups, and international exposure significantly enhances entrepreneurial success. Additionally, participation in non-curricular programs, support from Technology Transfer Offices, and core patents are associated with success, while interdisciplinary collaboration and serendipity often drive innovation. The study also reveals diverse motivations, academic backgrounds, and gender representation among founders, alongside a reliance on external networks despite a supportive local ecosystem. We anticipate that the findings gained will help inform policy and program design in the Queen’s ecosystem, and for other similarly structured to boost academic entrepreneurship and research commercialization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".