Embracing the Expanse: SmartSpecialization and Innovation in Canada’s Non-metropolitan Regions
Bibliographic record
Abstract
This paper introduces a framework for innovation-based regional economic development in Canada. The focus will be on non-metropolitan communities (i.e. rural or remote) and the relationship between those communities’ industries and post-secondary institutions (PSIs – i.e. universities, colleges, and polytechnics). The paper’s focus is based on the relative lack of attention that non-metropolitan communities receive in regards to policies that support or leverage innovation and R&D to strengthen their economic performance; and the possibility that the concept of Research and Innovation Strategies for Smart Specialization (RIS3) developed by the European Commission and OECD can be formally applied to the Canadian context. RIS3 is an approach that looks to foster regional development in a way that leverages the R&D strengths across multiple regions, and applies them in contextually appropriate ways to enhance local socio-economic productivity. RIS3 seeks to leverage local industrial and research strengths within a specific region, even when a region’s may be smaller (e.g. non-U15); or if it requires altering knowledge developed elsewhere and making it contextually appropriate for the region and its local industries. RIS3, developed in Europe, has not yet been adjusted to the Canadian context. This paper addresses the framework’s fit to the Canadian context, while also critically addressing: (i) The ability to deal with complexity and uncertainty; (ii) RIS3’s implicit assumption that private sector entrepreneurs will be present in the community to identify innovative opportunities; (iii) RIS3’s potential to encourage too much specialization; The need to strengthen networks of knowledge exchange between stakeholders.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".