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Record W3144432672 · doi:10.1080/15528014.2021.1890892

Feeding the Canadian Immigrant Family: an intersectional approach to meal preparation among immigrant families in Ontario

2021· article· en· W3144432672 on OpenAlexafffundabout
Eugena Kwon, Tracey L. Adams

Bibliographic record

VenueFood Culture & Society · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsWestern UniversitySaint Mary's University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsImmigrationEthnic groupContext (archaeology)IntersectionalitySociologyPromotion (chess)Qualitative researchGender studiesPolitical scienceGeographyPoliticsSocial science

Abstract

fetched live from OpenAlex

In light of the positive association between home-cooked meals and healthier diets, many recent health promotion strategies have encouraged the public to cook more often at home. However, class, race/ethnicity, and gender inequalities often intersect in shaping food practices and also impact who takes on the work involved in “feeding the family”. There are reasons to believe that challenges in healthy eating may be exacerbated among immigrants. Building on the prior research conducted in this area, this article draws on in-depth qualitative interviews with 23 married immigrant men and women to explore the social relations and social practices involved in feeding the immigrant family. By adopting an intersectional life course approach, we show how gender, immigrant status, race/ethnicity and economic hardship during integration processes all intersect to shape family decision making around meal preparation and eating practices. Our findings highlight that migration is a notable turning point that may influence the division of labor within immigrant families, and how the social and policy context of the host country shapes who takes primary responsibility for food work.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.235
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.029
GPT teacher head0.258
Teacher spread0.229 · 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 teacher head, not a consensus.

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

Citations6
Published2021
Admission routes3
Has abstractyes

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