Moral and aesthetic consecration and higher status consumers’ tastes: The “good” food revolution
Bibliographic record
Abstract
Research on the tastes of higher status groups has long prioritized analysis of aesthetic preferences. However, recent work has brought more attention to the moral dimensions of tastes. In this paper, we investigate the intersection of morality and aesthetics in tastes. Drawing on survey data and focus groups, we investigate how aesthetic and moral concerns operate in the domain of food, and meat specifically. A latent class analysis identifies four orientations to food that differ in their emphasis on aesthetic versus moral concerns. We identify classes that we label pragmatism, aestheticism, moralism, and moral aestheticism . These orientations toward moral and aesthetic concerns in food are associated with economic capital, cultural capital, age, political ideology, race, and gender. Respondents with higher social status are most likely to hold the moral aestheticism orientation, which simultaneously upholds moral and aesthetic concerns. Analysis of focus group data brings the nature of each of these four orientations into sharper focus. Further survey analyses show these four orientations predict high status aesthetic preferences and moral orientations beyond food, and they also predict the holding of symbolic and social boundaries related to moral judgments in food. We argue that research on high status cultural consumption must conceptualize and measure moral consecration alongside aesthetic consecration in order to better understand the social stratification of tastes.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".