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Record W3048955234 · doi:10.21083/surg.v12i1.5438

The Right to Food in Canada’s North: Food Security and Sustainability in Yukon Territory

2020· article· en· W3048955234 on OpenAlexaffvenueabout
Sophia Hou, Lauren Q. Sneyd

Bibliographic record

VenueSURG Journal · 2020
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsFood securitySustainabilityFood insecurityContext (archaeology)PovertyThreatened speciesSustainable agricultureAgricultureFood systemsStakeholderFood wasteGeographyBusinessPolitical scienceEconomic growthEconomicsPublic relationsEcology

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.421
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.021
GPT teacher head0.296
Teacher spread0.275 · 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 teacher head, not a consensus.

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
Published2020
Admission routes3
Has abstractyes

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