Arctic Innovation Hubs: Opportunities for Regional Co-operation and Collaboration in Oulu, Luleå, and Tromsø
Bibliographic record
Abstract
The Northern Review 45 (2017): 77–92https://doi.org/10.22584/nr45.2017.005Interest in Arctic issues has been growing in recent years. From an economic perspective, the Barents Region is of significant interest due to substantial investment projects. The European Union has strengthened its presence and influence in the region, playing a role in combatting climate change and optimizing opportunities for northern economic activity. Simultaneously, there have been intentions to narrow the gap between public policy and the private sector to more efficiently exploit business opportunities in the North. Promoting the Arctic’s potential for business development and building stronger co-operation between the region’s actors are among the recent activities in Arctic development. Innovation hubs generate new businesses from ideas and innovations. They operate in global networks by creating added value and attracting more investment capital and talent. This article explores innovation hubs in three regions in Northern Europe—Oulu (Finland), Luleå (Sweden), and Tromsø (Norway). The article examines, through an innovation hub framework, what kind of business development activities are generating growth in these innovation hubs, and what the differences are between these regions. This article discusses whether it is beneficial to have similar innovation service structures in every region, or if connected Arctic innovation hubs that strengthen Arctic co-operation is a better approach. More intensive co-operation between Arctic actors is most likely to require specific actions.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".