Canada's Ocean Supercluster initiative: A national policy in regional clothing?
Bibliographic record
Abstract
Abstract Since the late 1980s, there has been no explicit regional policy in Canada. Indirectly, though, equalization payments, industrial policies, as well as regional agencies encouraging the adoption of federal industrial and innovation policies, impact regional economies. In 2017, the federal government appeared to alter its approach: the Supercluster initiative was announced, drawing upon the idea that localized networks of interrelated firms can generate innovation and local development. In this paper, we discuss the mechanisms through which spatially focused industrial innovation policy can lead to regional development. We then focus on Canada's Ocean Supercluster initiative. The question we address is as follows: to what extent can this initiative (and, more widely, Canada's Supercluster policy) be understood as a regional development strategy driven by a coherent rationale for regional intervention? Apart from the fact that each Supercluster focuses on a pre‐existing core of firms located within a region, there is little evidence that the Supercluster initiative has regional development objectives or impacts.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".