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Record W4411414285 · doi:10.1080/17441692.2025.2516704

‘Keep learning, keep trying’: exploring food and cultural experiences and supports of Inuvialuit youth in the Inuvialuit Settlement Region

2025· article· en· W4411414285 on OpenAlexafffund
Maria Ramirez Prieto, Sonja Ostertag, Kanelsa Noksana, Shayla Arey, Denise Wolki, Susie Memogana, Celina Wolki, Aimee Yurris, Kelly Skinner

Bibliographic record

VenueGlobal Public Health · 2025
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsUniversity of Waterloo
FundersGlobal Water FuturesCanadian Institutes of Health ResearchPublic Health Agency of Canada
KeywordsPhotovoiceSubsistence agricultureThematic analysisFood securitySociologyReflexivityConsumption (sociology)Socioeconomic statusGeographyPopulationQualitative researchEconomic growthAgricultureSocial scienceEconomics

Abstract

fetched live from OpenAlex

Country food (CF) and subsistence harvesting are crucial for Inuvialuit of the Inuvialuit Settlement Region, contributing to food security, wellbeing and cultural continuity. However, youth face barriers to participating in these activities, with a shift away from CF consumption among younger generations. This community-based study used Photovoice to explore Inuvialuit youth experiences with CF to support Inuvialuit food security. Eleven co-researchers aged 13-30 documented their CF experiences through photographs and interviews. Reflexive thematic analysis identified five themes: (1) CF supports Inuvialuit youth wellbeing, (2) Preference for CF despite varied consumption and activity frequencies, (3) Network of CF within communities, (4) Strong foundational cultural knowledge and skills, and (5) Cultural continuity. These themes underscore the importance of CF in maintaining food security and Inuvialuit youth wellbeing.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.956
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0090.005
Scholarly communication0.0040.003
Open science0.0010.005
Research integrity0.0010.002
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 head0.131
GPT teacher head0.395
Teacher spread0.263 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

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