Maintaining Meat: Cultural Repertoires and the Meat Paradox in a Diverse Sociocultural Context
Bibliographic record
Abstract
Despite rising concerns about the meat industry and animal slaughter, meat consumption in Europe and North America remains relatively high, what has been called the “meat paradox.” In this article, we examine a diverse sample of Canadian meat eaters and vegetarians to build on earlier work on the psychological strategies people employ to justify eating meat. We analyze the explanations people give for meat eating within the context of what sociologists term cultural repertoires—the taken‐for‐granted, unarticulated scripts that inform actions. We distinguish between two types of repertoires: identity repertoires that have a basis in personal, embodied group identities and regularly draw from vivid first‐person experiences; and liberty repertoires that are more abstractly conceptualized and signal peoples' sense of their rights in social space. We find that these repertoires function in distinct ways, both in regard to how participants situated themselves within them, and in their capacity to facilitate active engagement with the ethical implications of conduct. Through these repertoires, we show how the meanings attributed to meat consumption are crucial for understanding its persistence in the face of strong reasons to change, while also advancing literature on cultural repertoires by highlighting their variability.
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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 | 0.004 |
| 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 | 0.031 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".