Bibliographic record
Abstract
This paper discusses how to improve Canada’s business innovation in order to boost labour productivity and output growth. Many general framework conditions are highly favourable to business risk-taking and innovation, including macro stability, openness, strong human capital, low corporate tax rates, low barriers to firm entry and flexible labour markets. However, they can be improved further by reduced external and interprovincial barriers in network and professional service sectors, more efficient capital markets, fewer capital tax distortions and improved patent protection. A second focus should be on ensuring that incentives arising from government subsidies are targeted on actual market failures. The very high level of support to business R&D via the federal Scientific Research and Experimental Development (SR&ED) tax credit and provincial top-ups may affect the incentives of small firms to grow and should be redesigned. A plethora of small, fragmented granting programmes, mainly geared to SMEs, should be streamlined for better government-business collaboration. The large public share in venture capital should be wound down, as it may crowd out more productive private finance. A final focus should be on boosting manager and worker skills that are intrinsic to all forms of innovation, by filling gaps in training, mentoring and education. This Working Paper relates to the 2012 OECD Economic Review of Canada (www.oecd.org/eco/surveys/Canada).
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".