Innovation Network Policy in Canada: Federal and Provincial Differences
Bibliographic record
Abstract
Innovation network policy is a type of industrial policy that first emerged in the 1980s. The goal of innovation network policy is to establish connections between private enterprises, universities, public research institutions, and other innovative organizations, thereby stimulating collaborative innovative activity. In Canada, both federal and provincial governments have fully embraced this form of industrial policy, forging inter-organizational connections through a variety of policy tools and programs. Some of these programs fund collaborative research projects and research consortia that bring together innovative organizations across the country. Others create and fund physical innovation spaces, such as innovation hubs, technology incubators, and science parks. Although many Canadian scholars have examined federal and provincial innovation network programs, none have ever compared them in a rigorous and systematic way. This dissertation seeks to conduct such a comparison; using qualitative research methods, it aims to determine whether federal and provincial innovation network programs are different from each other and, if so, why. The main finding is that federal and provincial programs are, in fact, different in two key ways. First, they target innovative organizations in different industries or areas of technology. Second, they both fund physical innovation spaces, but do so in different ways; federal programs provide these spaces with capital funding, while provincial programs provide them with operational funding. These differences in policy approach reflect the different geopolitical and historical-institutional realities facing federal and provincial governments.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.018 | 0.004 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".