Surviving as a Vegan in a World of Omnivores: Relational Fractures in Shared Practices
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.036 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".