Bibliographic record
Abstract
Canada's Regional Innovation Systems: The Science-based Industries, Jorge Niosi, Montreal & Kingston: McGill-Queen's University Press, 2005, pp. x, 171. Encouraging innovation in order to promote the growth of productivity and higher real incomes continues to be a centrepiece of economic policy in Canada. Canada's relatively poor performance in innovation and productivity growth compared to the United States remains a vexing problem in the minds of Canadian policymakers and academics. Notwithstanding much study of the issue, as well as policy initiatives designed to address the innovation and productivity gaps, it seems fair to say that we know much less than we should about the causes of and remedies for those gaps. In this respect, Jorge Niosi adds valuable information about how innovation activity has evolved over time in Canada. In particular, Niosi provides detailed discussions of the emergence and evolution in Canada of what has been called regional innovation systems (RIS). In the course of the discussions, he also offers some valuable policy insights with respect to promoting the growth of RIS.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.012 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".