The Right to Food in Canada’s North: Food Security and Sustainability in Yukon Territory
Bibliographic record
Abstract
This paper analyses the factors affecting food security in Yukon Territory. It was written for the Yukon Field School on Food Security offered at the University of Guelph in 2019. It utilizes Olivier De Schutter’s Right to Food Framework to examine elements of food availability, accessibility, and adequacy. Different perspectives from various stakeholder participants in the field school were gathered during guest lectures and on site visits and were cross-referenced with peer-reviewed sources to formulate conclusions on the right to food in Canada’s North. These perspectives suggest that the northern food supply is threatened by the effect of climate change on country food availability, the feasibility of local agriculture, and the provision of imported food. Additionally, the social barriers to country food and local food access in the context of high poverty rates also contribute to food insecurity. The implications of insufficient food availability and accessibility culminate in food inadequacy with notable consequences for physical, mental, and cultural health. Overall, a move towards a locally-sourced diet will likely play a key role in achieving sustainable food security in the north.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".