From Curiosity to Wealth Creation: How University Research can Boost Economic Growth
Bibliographic record
Abstract
Technological and scientific research are crucial to long-term economic growth. Canadians are three times as rich today as 50 years ago thanks to new products and processes. The source of technological innovation is research and development (R&D), most of which takes place in the private sector of the economy. University research, however, is the source of the basic building blocks of many of the core sectors of the economy, in everything from information technology to pharmaceuticals to much more. It is crucial for economic growth that the innovations that occur at Canadian universities get commercialized and find their way into the rest of economy. Canadian universities lag behind their US counterparts in generating technology transfer between academic research and companies. With innovation and productivity at the forefront of the Canadian public policy agenda, it is crucial that governments create the right incentives for university researchers to pursue research that can eventually be commercialized. Rather than governments directing researchers to pursue business-related research, the overarching priority for Canada should be to attract the best researchers in the world. Though it may seem paradoxical, the evidence supports the view that the greatest benefit to society will come from scientists for whom practical utility and individual financial reward are minor considerations. The best way to attract such scientists to Canada is to redirect our research support towards the problems that are most challenging from a scientific point of view, not towards those that bureaucrats view as most likely to lead to commercial success. Although the federal and provincial governments are taking steps to encourage the commercialization of research, they should go further by: • requiring that all federally funded research papers appear in open access online repositories; • developing a template available to all university researchers that outlines the terms of commercialization – such as intellectual property rights or revenues – between universities, researchers and their business partners; and • building on recent reforms to the National Research Council that make it more business-oriented, but with the eventual goal of making it a pan-Canadian technology transfer institution, leaving federal funding for research to granting organizations. Since business R&D has been falling as a share of the Canadian economy, and is a critical input to the commercialization of university research, it is also important that Canadian governments take measures to encourage Canadian businesses to invest in the commercialization process.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.026 |
| Scholarly communication | 0.021 | 0.014 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".