Networks for Science-Informed Innovation in the Arctic: Insights on the Structure and Evolution of a Canadian Research Network
Bibliographic record
Abstract
In remote peripheral regions like the Arctic, research networks have been identified as an important mechanism for nurturing science-informed innovation. Given that relatively little is known about the network structures that support Arctic innovation processes, we employ social network analysis techniques to examine the structural organization and evolution of ArcticNet, a large Canadian Arctic scientific research network over a 13-year period (2004 – 17). ArcticNet funded 152 multidisciplinary research teams, connecting multiple types of science-based innovation actors, not including students (301 organizations and 1659 individuals). The research network grew without reaching saturation (increasing size, decreasing density), suggesting that ArcticNet was successful in recruiting new actors over the 13-year period. ArcticNet was centralized around non-local, public-sector actors (mainly Canadian academics). The emergence of collaborations across several boundaries (sectoral, geographic, thematic) suggests that non-local Canadian academic actors played an important boundary-spanning role, particularly in the early stages of the network. Participation by local northern actors doubled from Phase 1 to Phase 4, and with time, local northern actors had an increasing propensity for carrying out boundary-spanning roles and addressing structural holes. This study presents new insights into the networked nature of Arctic scientific research with potential implications for future research and innovation policy.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".