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Record W2593660274 · doi:10.17730/0018-7259.76.1.15

Cultural Dimensions of Food Insecurity among Immigrants and Refugees

2017· article· en· W2593660274 on OpenAlexaboutno aff
Tina Moffat, Charlene Mohammed, K. Bruce Newbold

Bibliographic record

VenueHuman Organization · 2017
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationFood insecurityFood securityRefugeeAcculturationBusinessSociologyEconomic growthPolitical scienceGeographyEconomicsAgriculture

Abstract

fetched live from OpenAlex

Household food insecurity is experienced by many immigrants and refugees. Though culture strongly influences food and eating, little is known about the interaction between culture and immigrant food insecurity. Following Power's (2008) concept of “cultural food security,” we investigate three pillars of food security (food availability, access, and use) for immigrants and refugees living in a medium-sized city in Canada. Multiple perspectives on the challenges of obtaining and eating nutritious and culturally satisfying food were gathered through interviews with service providers and immigrants. Many immigrant participants identified issues that service providers did not, including a lack of availability of high quality, fresh, less processed, and chemical-free foods. Immigrant participants also expressed forms of food nostalgia. All participants identified low income and high food prices as barriers to accessing desired food. Also significant is the fact that immigrant participants experience difficultie...

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.001
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 head0.117
GPT teacher head0.431
Teacher spread0.314 · 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

Citations118
Published2017
Admission routes1
Has abstractyes

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