Harmonising Higher Education and Innovation Policies: Canada from an International Perspective
Bibliographic record
Abstract
Abstract This paper focuses on the relevance of harmonising higher education and innovation strategies in the context of fostering economic growth, illustrated by the particular weak point in the case of Canada. The present‐day market for highly‐skilled labour is global and therefore increasingly porous. A government that wishes to avoid losing its highly‐skilled workers to countries that can provide more attractive conditions must aim at investing simultaneously in tertiary education and science and engineering infrastructure. Ideally, supply (higher education) and demand side (innovation) policies would interact in a balanced way. Canada is located at the two extreme ends of investment in higher education and innovation and will be compared to other OECD countries. The paper concludes that seeking policy convergence in innovation and higher education with leading countries is not sufficient to reach growth and can produce disappointing results for talented people whose career expectations may remain unfulfilled. It is therefore crucial for a country to develop higher education and innovation ‘in harmony’ with the global context and also to achieve harmony between other policies and institutions in its own national context.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.012 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".