Commercialization of Academic Research in Canadian Universities: Optimizing Technology Transfer
Bibliographic record
Abstract
In recent years, in addition to the basic tenets of teaching and research, commercialization and innovation have become core priorities in higher education (Friedman & Silberman, 2003; Etzkowitz, 2003; Rasmussen et al., 2006). Universities have the right ingredients to be natural technology transfer incubators with a high influx of innovators and the capability to create new ventures and have high potential to generate a high level of economic development. Commercialization allows the results of innovative research to be utilized through transformation into marketable products or ‘technology transfer’. Since the 1980s, Canadian universities have begun dedicating resources and effort to discover how to best harness the innovation arising out of university-based research for knowledge transfer and revenue generation through commercialization. This thesis focuses on specific university inputs that influence the volume of technology transferred to industry through various commercialization channels and the impact each factor may have considering the institution size. Through data verified primarily from the Association of University Technology Managers’ (AUTM) annual surveys of Canadian and American universities from 2011 to 2015, this study analyzes the effect of administrative characteristics on technology transfer at a university. While the results of the study do not provide much conclusive guidance on the reasons behind growth in university-industry technology transfer, they do suggest that there is some greater effect in large universities that leads to more technology transfer activity than in smaller universities.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".