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Record W3130435016 · doi:10.1080/03670244.2021.1875455

The influence of cultural food security on cultural identity and well-being: a qualitative comparison between second-generation American and international students in the United States

2021· article· en· W3130435016 on OpenAlexaff
Kathrine E. Wright, Julie Lucero, Jenanne Ferguson, Michelle L. Granner, Paul G. Devereux, Jennifer Pearson, Eric Crosbie

Bibliographic record

VenueEcology of Food and Nutrition · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCulinary Culture and Tourism
Canadian institutionsMacEwan University
Fundersnot available
KeywordsFoodwaysEthnic groupFood insecurityThematic analysisCultural identityIdentity (music)Food securityPopulationSociologyQualitative researchPsychologySocial psychologySocial scienceGeographyAnthropologyAgricultureAestheticsDemography

Abstract

fetched live from OpenAlex

The purpose of this study was to explore the impact of cultural food insecurity on identity and well-being in second-generation American and international university students. Thirty-one semi-structured interviews were conducted from January-April 2020. Audio transcripts were analyzed using continuous and abductive thematic analysis. Students indicated that cultural foodways enhanced their well-being by facilitating their cultural/ethnic identity maintenance, connection, and expression. Conversely, cultural food insecurity diminished student well-being due to reduced cultural anchors, highlighting the importance of cultural food in this population. Universities that reduce cultural foodways barriers may mitigate cultural food insecurity for second-generation American and international university students. (100/100).

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)0.003
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.004
Scholarly communication0.0030.002
Open science0.0010.004
Research integrity0.0010.001
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 head0.036
GPT teacher head0.336
Teacher spread0.299 · 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

Citations16
Published2021
Admission routes1
Has abstractyes

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