Food in the cold: exploring food security and sovereignty in Whitehorse, Yukon
Bibliographic record
Abstract
Harsh weather patterns that are unpredictable owing to climate change, remoteness, dependence on food imports and limited local food production place Arctic and Subarctic food systems under serious pressure. The model of food sovereignty provides a surprisingly interesting contribution to address the food insecurity in these regions; it promotes long-term stable provision of healthy foods (sustainable) that are accessible to all (equity) and fosters local food production-consumption patterns (localisation). This study aims to deepen the understanding of food insecurity in the Subarctic regions and explores the possibilities for a food sovereignty approach at both individual and regional level. The study focuses on Whitehorse, capital of Yukon, Canada, and uses a cross-sectional online survey among residents of Whitehorse and semi-structured in-depth interviews with food-systems experts in Yukon. The findings indicated a need for affordable year-round local food production. Application of food sovereignty has provided the opportunities for local food procurement, innovation hubs, and several types of greenhouses including hydroponics and vertical farming, to work towards a more localised food system, thereby improving food security and sovereignty in Yukon. The findings constitute the scientific knowledge base for the formulation of prospective scenarios in the spirit of the food sovereignty theory.
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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 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".