A study of university-industry linkages in the biotechnology industry: perspectives from Canada
Bibliographic record
Abstract
This study focuses on the trends in university-industry linkages in the Canadian biotechnology industry. Technology transfer from university to industry has been a main component in biotechnology innovation. In most industrialised countries, the government has played a role in the development of university-industry relationships. Studies have focused on the pros and cons of university-industry linkages as well as the impact of government funding and regulation on high technology innovation. This paper seeks to understand the patterns of university-industry collaboration, factors influencing such collaboration and the role of government support in university-industry partnerships in three main regions of biotechnology innovation in Canada. These three areas are Montreal, Toronto and Vancouver. Results show that most collaborations within Canada are with local universities as well as with foreign universities. Ontario-based firms are driven by product development whereas Montreal and Vancouver based firms are motivated by their access to university scientists and university research. Government support is acknowledged for firm-based research and technology transfer.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.021 |
| Science and technology studies | 0.018 | 0.004 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 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".