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Record W2080163136 · doi:10.3402/ijch.v65i4.18117

Traditional and market food access in Arctic Canada is affected by economic factors

2006· article· en· W2080163136 on OpenAlexaffabout
Jill Lambden, Olivier Receveur, Joan Marshall, Harriet V. Kuhnlein

Bibliographic record

VenueInternational Journal of Circumpolar Health · 2006
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsUniversité de MontréalMcGill University
Fundersnot available
KeywordsCircumpolar starFishingArcticIndigenousGeographyCommercial fishingFood securitySocioeconomicsPopulationThe arcticMarket accessEnvironmental healthFisheryBusinessMedicineEconomicsEcologyAgriculture

Abstract

fetched live from OpenAlex

OBJECTIVES: This study aimed to evaluate the access that Indigenous women have to traditional and market foods in 44 communities across Arctic Canada. STUDY DESIGN: This secondary data analysis used a cross-sectional survey of 1771 Yukon First Nations, Dene/Métis and Inuit women stratified by age. METHODS: Socio-cultural questionnaires were used to investigate food access and chi-square testing was used to ascertain the distribution of subject responses by age and region. RESULTS: There was considerable regional variation in the ability to afford adequate food, with between 40% and 70% saying they could afford enough food. Similarly, regional variation was reflected in the percentage of the population who could afford, or had access to, hunting or fishing equipment. Up to 50% of the responses indicated inadequate access to fishing and hunting equipment, and up to 46% of participants said they could not afford to go hunting or fishing. CONCLUSIONS: Affordability of market food and accessibility to hunting and fishing in Arctic Canada were major barriers to Indigenous women's food security.

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)0.000
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 score0.014
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.032
GPT teacher head0.344
Teacher spread0.312 · 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

Citations131
Published2006
Admission routes2
Has abstractyes

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