Bibliographic record
Abstract
As Canada enters the twenty-first century, it faces several major challenges.One is the consolidation of its national system of innovation (NSI) -that is, the system composed of its innovating firms, universities, and public laboratories, together with the institutions (public and private) that finance innovation.This system developed slowly after Confederation and during the first four decades of the twentieth century, and it has experienced rapid growth in the last sixty years.It may suffer from several gaps and inefficiencies, including overlapping of governmental jurisdictions, duplication of some corporate efforts, missing elements, and some lack of coordination.Nevertheless, it has been a major contributor to Canada's prosperity in the postwar period and may become the most decisive factor of its prosperity in the future.It is now challenged by governments' budgetary priorities.This book is a tentative portrait of the state of the system of innovation in the mid-iggos.Its first goal is to identify its major strengths and weaknesses and its core elements.Its second, theoretical goal is to develop, refine, and apply the concept of NSI, which seems key to the understanding of present and future trends in economic development.I try to link the concept with theories of endogenous growth, competence perspectives, and evolutionary economics.Chapter i is thus devoted to theory about NSIS.In part I, chapter 2 traces the origins and evolution of Canada's NSI, and chapters 3-5 study its domestic system of research and development (R&D).In part II, chapters 6-8 analyse the internationalization of Canadian R&D and inquire into the possible eventual development of a North American
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.409 | 0.203 |
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".