MétaCan
Menu
Back to cohort
Record W2216993941 · doi:10.2752/175174408x347900

Consuming Food and Constructing Identities among Arabic and South Asian Immigrant Women

2008· article· en· W2216993941 on OpenAlexaffabout
Helen Vallianatos, Kim D. Raine

Bibliographic record

VenueFood Culture & Society · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsFoodwaysEthnic groupImmigrationContext (archaeology)Focus groupVariety (cybernetics)ArabicIdentity (music)Consumption (sociology)SociologyGender studiesGeographySocial scienceAnthropology

Abstract

fetched live from OpenAlex

Migration to a new country often results in a variety of social and economic challenges, often reflected in foodways. Food is of central importance in maintaining connections to home, and signifying ethnic identity among diasporic community members. Alternatively, new opportunities may be represented by the incorporation of new food elements into consumption patterns. Focus group interviews conducted with Arabic and South Asian immigrant women residing in a smaller Canadian city reveal the meanings women imparted to their own and their families' food choices and dietary habits. Women shared their struggles of maintaining ethnic cuisine as a marker of community affiliation while to varying degrees, integrating new foods, usually at their children's request. Experiences were not uniform, yet comparisons within and across these two communities suggest the importance of local social factors and politico-economic context in shaping commonly shared food and migration experiences and such shared realities highlight areas for advocacy.

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)0.002
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 score0.022
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.004
Scholarly communication0.0030.001
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.017
GPT teacher head0.231
Teacher spread0.214 · 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

Citations160
Published2008
Admission routes2
Has abstractyes

Explore more

Same venueFood Culture & SocietySame topicMigration, Ethnicity, and EconomyFrench-language works237,207