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Record W2119288938 · doi:10.3402/ijch.v65i5.18132

Food security in Nunavut, Canada: barriers and recommendations.

2006· article· en· W2119288938 on OpenAlexafffundabout
Hing Man Chan, Karen Fediuk, Sue Hamilton, Laura Rostas, Amy Caughey, Harriet V. Kuhnlein, Grace M. Egeland, Eric Loring

Bibliographic record

VenueInternational Journal of Circumpolar Health · 2006
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsInuit Tapiriit KanatamiGovernment of NunavutMcGill UniversityUniversity of Northern British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchHealth Canada
KeywordsFocus groupFood securityBusinessEnvironmental healthGeographyConsumption (sociology)Psychological interventionMedicineMarketingSociologyNursingAgriculture

Abstract

fetched live from OpenAlex

OBJECTIVES: The food supply of Inuit living in Nunavut, Canada, is characterized by market food of relatively low nutritional value and nutrient-dense traditional food. The objective of this study is to assess community perceptions about the availability and accessibility of traditional and market foods in Nunavut. STUDY DESIGN: A qualitative study using focus group methodology. METHODS: Focus groups were conducted in 6 communities in Nunavut in 2004 and collected information was analyzed. RESULTS: Barriers to increased traditional food consumption included high costs of hunting and changes in lifestyle and cultural practices. Participants suggested that food security could be gained through increased economic support for local community hunts, freezers and education programs, as well as better access to cheaper and higher quality market food. CONCLUSIONS: Interventions to improve the dietary quality of Nunavut residents are discussed.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.120

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0060.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.017
GPT teacher head0.345
Teacher spread0.329 · 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

Citations207
Published2006
Admission routes3
Has abstractyes

Explore more

Same venueInternational Journal of Circumpolar HealthSame topicIndigenous Studies and EcologyFrench-language works237,207