Understanding the food preferences of people of low socioeconomic status
Bibliographic record
Abstract
Scholars have long studied consumer taste dynamics within class-stratified contexts, but relatively little attention has been paid to the taste preferences of low-socioeconomic-status groups. We analyze interview data from 254 individuals from 105 families across Canada to explore the cultural repertoires that guide low-socioeconomic-status consumer tastes in food. Empirically, we ask which foods respondents prefer, and for what reasons, across socioeconomic status groups. Analytically, we argue that low-socioeconomic-status respondents demonstrate aesthetic preferences that operate according to four cultural repertoires that are distinctly different from that of high-socioeconomic-status omnivorous cultural consumption. Our respondents display tastes for foods from corporate brands, familiar “ethnic” foods, and foods perceived as healthy. While low-socioeconomic-status taste preferences in food are shaped by quotidian economic constraints – what Bourdieu called “tastes of necessity” – we show how cultural repertoires guiding low-socioeconomic-status tastes relate to both material circumstances and broader socio-temporal contexts. Our findings advance debates about the nature of low-socioeconomic-status food ideals by illuminating their underlying meanings and justifications and contribute to scholarly understanding of low-socioeconomic-status consumption.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".