Northern Food Networks: Building Collaborative Efforts for Food Security in Remote Canadian Aboriginal Communities
Bibliographic record
Abstract
Canada's northern and remote regions experience high rates of food insecurity, exceptionally high food costs, environmental concerns related to contamination and climate change, and a diversity of other uniquely northern challenges related to food production, acquisition, and consumption. As such, there is a need to understand and develop strategies to address food-related concerns in the North. The diversity of communities across the North demands the tailoring of specific, local-level responses to meet diverse needs. Over the past decade, local networks have emerged as a powerful method for developing localized responses, promoting food security and the development of more sustainable food systems across Canada and North America. Despite this, there is a paucity of research examining challenges and effective approaches utilized by these local networks or their potential applicability for building food security in rural, remote, and northern communities. This research utilized participant observation as a method to examine the experiences of a Northern Canadian food security network. The experience of this network points to strategies that can lead to successful collaborative approaches aimed at implementing programs to address food security in northern and remote communities.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.035 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".