Transitioning to a vegetarian or vegan diet: fractures and continuities in biography and practice
Bibliographic record
Abstract
This empirical paper explores the transition to a vegetarian or a vegan diet, at the meeting point of individual biographies and their social context. It is based on 13 interviews conducted with 14 participants in the province of Quebec, Canada. It mobilises a conceptual framework that couples a social practice approach (SPT) with the concept of ‘fractures’ as developed by O’Neill and colleagues (2019). The results show that the transition to a vegetarian or a vegan diet involves tastes, ethical concerns and skills that were formed since childhood, and that it also depends on the interaction of elements specific to a social context such as a supportive social environment or the availability of meat replacement products. The participants’ experiences also suggest that transitions can be sparked by life events such as a new friendship, a new relationship or moving out of the parents’ house, all of which have in common a transformation in the social relations and networks central to everyday life. The conclusion discusses the role played by time, social relationships and space in the participants’ accounts and how it can be read through the lens of SPT and ‘fractures’, to understand how individual experience can be tied to change on a larger scale.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.023 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".