MétaCan
Menu
Back to cohort
Record W2901393264 · doi:10.1080/19320248.2018.1537867

The Community Food Environment and Food Insecurity in Sioux Lookout, Ontario: Understanding the Relationships between Food, Health, and Place

2018· article· en· W2901393264 on OpenAlexafffundabout
Barbara Parker, Kristin Burnett, Travis Hay, Kelly Skinner

Bibliographic record

VenueJournal of Hunger & Environmental Nutrition · 2018
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsUniversity of WaterlooYork UniversityLakehead University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsFood insecurityIndigenousFriendshipFood securityCommunity healthFood systemsPolitical scienceSociologyEnvironmental healthEconomic growthGeographyHealth careSocial scienceMedicineEcologyEconomicsAgriculture

Abstract

fetched live from OpenAlex

The purpose of this community-based research project was to explore the community food environment and food insecurity in the northern Ontario community of Sioux Lookout. Previous informal conversations with local organizations identified food insecurity as a serious concern with particularly impactful consequences for Indigenous well-being. Community members were invited to a feast at the Friendship Centre where individuals participated in surveys (N = 76) and talking circles (N = 100) around the question: “When it comes to food what do you think is the most important issue in Sioux Lookout?” Four related themes emerged including: (1) The Community Food Environment; (2) Indigenous Food Knowledge; (3) Concerns about Health; and (4) Moving Forward. This article considers the community food environment and the relationships between people, food, culture, health, and place.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.139

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.005
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.252
GPT teacher head0.361
Teacher spread0.109 · 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

Citations16
Published2018
Admission routes3
Has abstractyes

Explore more

Same venueJournal of Hunger & Environmental NutritionSame topicFood Security and Health in Diverse PopulationsFrench-language works237,207