Food Security Challenges and Opportunities in Rural Yukon Communities
Bibliographic record
Abstract
Northern and Indigenous rural communities in the Yukon face unique food security challenges as a result of higher costs of living, fewer services, loss of traditional practices and remoteness. Food in Place will discuss some of these challenges drawing on a recent paper published by Dr. Sara McPhee-Knowles and David Gattensby as well as anecdotal evidence collected through community engagement sessions. The vulnerability of these communities also presents an opportunity for innovative food programming and community driven food systems change. This is demonstrated by the success of the Yukon First Nation Education Directorate’s Rural Nutrition Program in addressing food security while creating a culture around food and wellness. Other potential community-driven initiatives are explored such as the building of community wild meat processing facilities in rural communities that would reduce barriers to using wild meat in community food programs. We will also explore opportunities to increase innovation through targeted funding streams that will allow rural communities to step into their full potential as leaders in food systems change.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".