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Record W4386023122 · doi:10.14430/arctic77822

Community Perspectives on Inuit Country Food Insecurity in Gjoa Haven, Nunavut

2023· article· en· W4386023122 on OpenAlexfundvenueaboutno aff
J. E. Leo Desautels, Jacqueline M. Chapman, Stephan Schott

Bibliographic record

VenueARCTIC · 2023
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
FundersPolar Knowledge CanadaGenome Canada
KeywordsFood securityFood insecurityContext (archaeology)Focus groupHavenPolitical scienceEconomic growthArcticDemographicsFood systemsBusinessGeographySociologyMarketingEconomicsEcologyAgriculture

Abstract

fetched live from OpenAlex

This paper explores how policies and programs can better support country food security and food sovereignty in Gjoa Haven, Nunavut. Through a series of six focus groups with a total of 74 participants, we explore the challenges that Elders, youth, hunters, food preparers, and program providers face in the access, availability, quality, and use of country food. Despite the diverse representation among focus groups, participants revealed similar challenges across demographics and highlighted how tailored policies and programs can provide complementary solutions that serve more than one purpose. We argue that policies and programs targeting financial and economic challenges; resources and infrastructure; and skills and knowledge will improve country food security and will promote food sovereignty. Ultimately, policies and programs must be community informed and tailored to their current context and community dynamics. However, the recommendations we provide could be adapted to other Arctic communities experiencing similar challenges.

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)0.003
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.304
Threshold uncertainty score0.611

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0240.007
Scholarly communication0.0050.002
Open science0.0010.007
Research integrity0.0010.002
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 head0.065
GPT teacher head0.382
Teacher spread0.317 · 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 designQualitative
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

Citations3
Published2023
Admission routes3
Has abstractyes

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