Bibliographic record
Abstract
Canada is widely considered to be an innovation under-achiever, despite decades-long attempts to address this gap. In light of this criticism, this Commentary reviews the range of policy tools that governments in advanced economies have at their disposal to foster innovation. It takes a holistic approach to innovation policy in that many of the policy areas covered in this report are not primarily designed to spur innovation per se, but nevertheless can have a significant impact on it. Innovation policy is less likely to succeed if it does not carefully integrate measures affecting the four essential ingredients of talent and knowledge, entrepreneurship and business growth, innovation in government, and clarity of purpose for government support. This entails, but is not limited to, adopting government framework policies to encourage innovation, such as a pro-innovation tax system, trade policy, intellectual-property regime, competition policy, and approach to regulation, as well as fostering acceptance of innovation in civil society and the general public. Key areas for potential improvement, ultimately contributing to raising Canadians’ standards of living, include: • a greater focus on research and educational excellence, and on deploying and attracting related talent and skills, including those beyond scientific and engineering skills, such as marketing and business; • a suite of trade, fiscal, regulatory and other policies and approaches to: 1) foster entrepreneurship and economic activity based on existing talent, skills and this research; 2) facilitate the risk-taking – and acceptance of risk-taking – that such activities entail; and 3) remove unnecessary barriers to these activities; • innovation in the delivery of public services themselves; and • a more goals-oriented approach to government support for business innovation that nevertheless relies more on market and other arm’s length mechanisms, as well as international collaboration in some areas, to achieve desired goals. The ultimate motivation for wanting to improve Canada’s innovation performance is simple: to improve Canadians’ overall standards of living.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.022 | 0.013 |
| Scholarly communication | 0.019 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".