Climate change, community capitals, and food security: Building a more sustainable food system in a northern Canadian boreal community
Bibliographic record
Abstract
Canada’s North offers unique food systems perspectives. Built on close cultural and spiritual ties to the land, the food systems within many northern communities still rely on the harvesting and gathering of traditional food and function through the sharing of food throughout the community. However, social, economic and environmental pressures have meant that some communities rely more on food purchased from the stores, which can be unhealthy and expensive, leading to high rates of food insecurity and chronic health problems in many communities in the North. Northern communities are now dealing with the impacts of climate change that are increasing pressure on the food system by limiting both access to the land and the availability of traditional food sources. This research presents a case study from the Northern Canadian boreal community of Kakisa, Northwest Territories. Using a Participatory Action Research (PAR) methodology, community members play an active role in identifying threats to the community food system, as well as developing community-based solutions to foster adaptation and transformation of their food systems to become more resilient to the impacts of climate change. By using the Community Capitals Framework to identify multiple stressors on the food system this research illustrates how a community can allocate available capitals to adapt to the impacts of climate change as well as identify which capitals are required to build a more sustainable food system.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.037 | 0.009 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".