Policy Rationale for Innovation Parks in Canada
Bibliographic record
Abstract
Innovation Parks became an innovation and economic development policy instrument in the Western world more than two decades ago. While Canada was slow to catch up to this phenomenon, it did eventually join the trend. This study analyzes the policy rationales for innovation parks in Canada through a national and sub-national lens. For this purpose, Ontario and Saskatchewan are chosen as comparative points. It compares the Saskatchewan Innovation Place (SIP), McMaster Innovation Park (MIP), and David Johnston Research and Technology Park (DJRTP). The study develops a three-pronged analysis of institutions, interests and ideas to explain why governments support innovation parks as a policy instrument. It is argued that the continued support of these initiatives is largely a function of institutional path-dependence and policy lock-ins manifest through sunk infrastructure investments, desire to balance different interest groups – mainly the commercial real estate sector and the organizations representing the research parks. These institutional and structural struggles are underpinned by the ideational frames of economic development and knowledge-based economic growth.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.024 | 0.007 |
| Scholarly communication | 0.017 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.015 | 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".