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Record W2061671037 · doi:10.1108/yc-08-2013-00390

Influences on food choices of urban Chinese teenagers

2014· article· en· W2061671037 on OpenAlexaff
Ann Veeck, Yu Fang, Hongyan Yu, Gregory Veeck, James W. Gentry

Bibliographic record

VenueYoung Consumers Insight and Ideas for Responsible Marketers · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCulinary Culture and Tourism
Canadian institutionsBrock University
Fundersnot available
KeywordsFriendshipTastePsychologyFood choiceAffect (linguistics)Context (archaeology)OriginalityValue (mathematics)ChinaSocial environmentSocial influenceMarketingSocial psychologyDevelopmental psychologyBusinessMedicineSociologyGeographyCommunication

Abstract

fetched live from OpenAlex

Purpose – This study aims to examine the major influences of food choices of Chinese teenagers within a dynamic food marketing environment. Design/methodology/approach – The paper reports findings from semi-structured interviews with high school students which examine teenagers’ guidelines for selecting food, along with their actual eating behavior. Findings – The results reflect on how four major influences – personal, family, peer and retailer – may intersect to affect the eating behaviors of Chinese adolescents, as they navigate an intense education schedule during a time of rapidly changing cultural values. Different norms of food choice – nutrition, food safety, taste, body image, price, convenience, sharing, friendship and fun – are evoked according to the social context and concurrent activities of the teenagers. Social implications – The findings offer tentative insights related to the potential for promoting healthier eating habits for adolescents in urban areas of China. Originality/value – The study demonstrates how, within this rapidly changing food environment, food retailers are creating alliances with teenagers to meet needs of convenience, speed, taste and social interaction.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.283
Threshold uncertainty score0.349

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.011
GPT teacher head0.226
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 teacher head, 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

Citations29
Published2014
Admission routes1
Has abstractyes

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