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Record W4415586847 · doi:10.21083/crrf.v36i1.8111

Food Security Challenges and Opportunities in Rural Yukon Communities

2025· article· W4415586847 on OpenAlexaffabout
Michelle Watson, Simone Rudge, Sarah McPhee-Knowles, Kim Rumley, Catherine Littlefield, Nicole S. Hutton

Bibliographic record

VenueProceedings of the Canadian Rural Revitalization Foundation · 2025
Typearticle
Language
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsYukon University
Fundersnot available
KeywordsFood securityIndigenousFood systemsVulnerability (computing)Rural areaFace (sociological concept)Food processingFood insecurity

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.790
Threshold uncertainty score0.417

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0160.004
Scholarly communication0.0030.002
Open science0.0010.008
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.181
GPT teacher head0.371
Teacher spread0.190 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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