Greening Canada’s Arctic food system: Local food procurement strategies for combating food insecurity
Bibliographic record
Abstract
Across northern Canada community gardens and greenhouses are being used as alternatives to imported foods that are often unaffordable, are of compromised quality, or simply unavailable in local retail outlets. Community gardens and greenhouses are seen as part of the solution to lessen local reliance on costly nutrient-poor market foods imported from the south. In spite of their acknowledged benefits, research on community gardens and greenhouses in northern Canada, including their numbers and locations, remains sparse and anecdotal. The objectives of this research were to inventory and map community gardens and greenhouses in northern Canada, encompassing Labrador, Nunavik, Nunavut, Yukon, and the Northwest Territories. This inventory represents an initial stage of research that will determine the extent to which community gardens and greenhouses, as local procurement strategies, are meeting the food needs of northern residents. This research is part of a circumpolar research project supported by the Arctic Council’s Sustainable Development Working Group, which is examining the opportunities for the Arctic to become a self-sustaining food-producing region.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| 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".