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Record W7127131654 · doi:10.18357/wg24202224

Examining Food Security in Inuit Communities

2022· article· W7127131654 on OpenAlexafffundabout
Deanna Andreschefski, Megan Fisk

Bibliographic record

VenueWestern Geography · 2022
Typearticle
Language
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsUniversity of Lethbridge
FundersUniversity of Lethbridge
KeywordsFood securityFood insecurityUnemploymentSocioeconomic statusFood systemsFood prices

Abstract

fetched live from OpenAlex

Food insecurity in Inuit communities in Canada is an increasing concern for Inuit families and, as a complex issue, needs to be fully understood to be properly addressed. We analyzed peer-reviewed articles from the University of Lethbridge data base to understand the complexities of the Inuit food security issue. Inuit people are in a time of nutritional transition, as they move from nutrient-dense traditional foods to highly processed nutrient-deficient westernized foods, compromising food accessibility and affordability. Food insecurity is exacerbated by intersecting socioeconomic and environmental factors, including high rates of unemployment and poverty, high food prices in grocery stores, an increase in the frequency of extreme weather events, declining animal populations, altered seasonal sea ice cycles, and failed attempts to mitigate some of these issues. This paper will discuss each of the factors and their health impacts and will identify possible solutions to the issue of Inuit food insecurity.

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)0.007
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 score0.068
Threshold uncertainty score0.137

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.006
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0010.002
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 head0.068
GPT teacher head0.319
Teacher spread0.251 · 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

Citations0
Published2022
Admission routes3
Has abstractyes

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