Good food, good people: Understanding the cultural repertoire of ethical eating
Bibliographic record
Abstract
Ethical consumption is understood by scholars as a key way that individuals can address social and ecological problems. While a hopeful trend, it raises the question of whether ethical consumption is primarily an elite social practice, especially since niche markets for ethical food products (for example, organics, fair trade) are thought to attract wealthy, educated consumers. Scholars do not fully understand the extent to which privileged populations think about food ethics in everyday shopping, or how groups with limited resources conceptualize ethical consumption. To address these knowledge gaps, the first goal of this paper is to better understand how consumers from different class backgrounds understand ethical eating and work these ideas into everyday food practices. We draw from 40 in-depth interviews with 20 families in two Toronto neighborhoods. Our second goal is to investigate which participants have privileged access to ethical eating, and which participants appear relatively marginalized. Drawing conceptually from cultural sociology, we explore how ethical eating constitutes a cultural repertoire shaped by factors such as class and ethno-cultural background, and how symbolic boundaries are drawn through eating practices. We find that privilege does appear to facilitate access to dominant ethical eating repertoires, and that environmental considerations figure strongly in these repertoires. While low income and racialized communities draw less on dominant ethical eating repertoires, their eating practices are by no means amoral; we document creative adaptations of dominant ethical eating repertoires to fit low income circumstances, as well as the use of different cultural frameworks to address moral issues around eating.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.060 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".