Fostering innovation in Arctic food industries
Bibliographic record
Abstract
This Field Report describes the stages in the development of the Arctic Food Innovation Cluster (AFIC). Motivation for AFIC arose during research supported by the Arctic Council’s Sustainable Development Working Group, which found the development of Arctic food industries was constrained by a general absence of innovation in primary and secondary product development. Through a series of iterative stages—scoping, consultations, design—a vision for AFIC emerged. This involved the establishment of a central AFIC hub that would promote strategic coordination, direction, and knowledge mobilization between stakeholders. The High North Centre (HNC) for Business and Governance at Nord University in Norway has assumed this central role and will guide the development of the AFIC initiative. The AFIC strategy assumes development of a network of regional pan-Arctic food hubs that will serve as aggregation points for knowledge sharing and strengthening the interconnectivity between local food producers and other value chain actors in the Arctic food system. Ultimately, the goal of AFIC and its associated regional hubs is to help instill a sense of pride, empowerment, health, and wellbeing in Arctic communities through the sustainable development of Arctic food industries.
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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 | 0.013 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.002 |
| 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".