Academic Entrepreneurship and Faculty Engagement with Industry In Canadian University Schools of Engineering
Bibliographic record
Abstract
This study set out to explore and investigate academic entrepreneurship among the members of the professoriate holding continuing, full-time appointments in Canadian university faculties of engineering and applied science. The primary thesis advanced in this study is that faculty involvement in academic entrepreneurship can be explained (in part) by institutional, individual, and occupational factors. Academic entrepreneurship generates two distinct faculty behaviours or processes: the engagement with industry and the commercialisation of research. A conceptual model is used to identify potential antecedents and consequences of faculty involvement in both forms of academic entrepreneurship. Data from 379 respondents are collected using an online questionnaire and the study questions are addressed using correlational, step-wise multiple regression, and confidence interval testing. Findings from this study show that faculty engagement with industry and faculty research commercialisation are conceptually distinct yet strongly related in practice. The strength of industry collaboration is found to be strongly associated with the assessment of its benefits and costs. Occupational characteristics of research faculty are found to be robust predictors of both industry engagement and research commercialisation. The pattern of faculty-industry relationships, faculty research orientation and research identity, and institutional and job-related factors are strong predictors undergirding faculty motivation to collaborate with industry, the diversity and strength of industry engagement in practice, and research commercialisation activities. Scientists that engage more extensively with industry demonstrate better research performance outcomes than those less engaged.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".