Innovation and Knowledge Creation in an Open Economy: Canadian Industry and International Implications
Bibliographic record
Abstract
The endogenous growth literature has placed knowledge creation at the forefront of the research agenda. Long run economic growth is sustained because of the continuous creation of knowledge. To understand the dynamics of knowledge one must pay particular attention to the determinants of research and development (R&D) and innovation. Baldwin and Hanel, who are two experts of the economics of innovation in Canada, have produced a well‐written and informative book on the extent to which innovation in Canada is a function of three main dimensions: firm size, industry and nationality of the firms’ owners. The subject of the book is important for Canada whose R&D intensity is below both the European Union and OECD average. Moreover using Canada, a small open economy, as a case study is of particular interest both as a comparison with larger economies and as a lesson for other countries with similar characteristics. Baldwin and Hanel make good use of the 1993 Canadian Innovation and Advanced Technology Survey (SIAT) to provide well‐documented answers to their question. As the authors point out, innovation surveys must be able to distinguish across different types of innovations and allow the correlation between innovative input and output to vary by firms’ characteristics. The SIAT, which asks questions about a firm’s innovative capacities and focuses on the firm’s major innovation, is indeed better suited than other surveys to address innovations. Most other innovation surveys only prompt firms to report whether they have innovated – irrespective of the importance of the innovation. The authors convincingly argue that the innovation approach is better suited because a firm can better answer questions related to one innovation but the very often it does not amass data on all innovations.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.017 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".