Problematizing “ethical eating”: the role of policy in an ethical food system
Bibliographic record
Abstract
The concept of “ethical eating” has become prominent within public discourse. It refers to a form of ethical consumption whereby consumers can feel that they have directly impacted the food system through their food choices. However, the terms and practices often used to define “ethical eating” are incomplete and exclude other ethical issues within the food system that are more complex and have less clear solutions than those offered through ethical consumption. Through a content analysis of 100 newspaper and magazine articles discussing the practice of “ethical eating,” as well as a review of literature on this topic, this article argues that issues within the food system cannot be solved through ethical consumption alone. Instead, there must be an increased role for public policy to address ethical concerns within the food system. Through examining organic, meat-avoidant, fair trade, and local diets as the most frequently mentioned terms associated with “ethical eating,” three case studies are presented highlighting the tensions associated with access to eating ethically, Indigenous food sovereignty, and production of ethical food. This paper argues that addressing these ethical issues requires public policy to tackle the root causes and ensure all are served within the food system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.081 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.023 | 0.165 |
| Scholarly communication | 0.031 | 0.036 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.017 | 0.019 |
| 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".