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Record W3175319196 · doi:10.1080/15528014.2021.1939960

Problematizing “ethical eating”: the role of policy in an ethical food system

2021· article· en· W3175319196 on OpenAlexaff
Daniela Spagnuolo

Bibliographic record

VenueFood Culture & Society · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicOrganic Food and Agriculture
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsFood systemsEthical issuesEnvironmental ethicsEngineering ethicsPolitical sciencePsychologySociologyFood securityPhilosophyGeographyEngineeringAgriculture

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.681
Threshold uncertainty score0.766

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.229
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2021
Admission routes1
Has abstractyes

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