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Record W4413910370 · doi:10.1093/jcr/ucaf052

Surviving as a Vegan in a World of Omnivores: Relational Fractures in Shared Practices

2025· article· en· W4413910370 on OpenAlexafffund
Aya Aboelenien, Zeynep Arsel

Bibliographic record

VenueJournal of Consumer Research · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicOrganic Food and Agriculture
Canadian institutionsConcordia UniversityHEC Montréal
FundersSocial Sciences and Humanities Research Council of CanadaConcordia University
KeywordsOmnivoreVegan DietAdvertisingBusinessBiologyMedicineEcology

Abstract

fetched live from OpenAlex

Abstract Prior research documents the role of the misalignment of practice elements in practice habituation and change. We extend this literature by demonstrating the understudied role of practice relationality. Locating our empirical work in veganism, a context that encompasses a bundle of interrelated practices, we show how people who adopt veganism manage the relationality of their food-related practices (e.g., eating, cooking, and shopping for food) during shared moments. Building on interview, secondary, and netnographic data on people who pursue veganism, we demonstrate that changes in shared practice performances cause relational fractures. We pinpoint relational fractures that hinder practitioners from smoothly performing shared practices in three contexts: co-performance, co-learning, and the marketplace. To repair practice relationality, vegan consumers enact four relational competences: decoding, decoupling, divesting, and chameleoning. These competences can repair some relational fractures while aggravating others. When vegan consumers fail to acquire any competence, however, they revert to their old omnivorous performances. Our article contributes to practice theory by conceptualizing the role of practice relationality in practices, introducing the concept of relational competence as a necessary element for performance (re)rehabituation, and demonstrating the role of practice intelligibility in the co-performance of shared practices.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.250
Threshold uncertainty score0.846

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.079
GPT teacher head0.389
Teacher spread0.309 · 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

Citations2
Published2025
Admission routes2
Has abstractyes

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