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Record W1852396658 · doi:10.22439/fs.v0i9.3060

Foucault and the Ethics of Eating

2010· article· en· W1852396658 on OpenAlexaff
Chloë Taylor

Bibliographic record

VenueFoucault Studies · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsArgument (complex analysis)ConstitutionPleasureSociologyHuman sexualityTransformative learningIdentity (music)DisciplineAestheticsGender studiesEnvironmental ethicsLawPhilosophySocial sciencePsychologyPolitical science

Abstract

fetched live from OpenAlex

In a 1983 interview, Michel Foucault contrasts our contemporary interest in sexual identity with the ancient Greek preoccupation with diet, arguing that sex has replaced food as the privileged medium of self-constitution in the modern West. In the same interview, Foucault argues that modern liberation movements should return to the ancient model of ethics, of which diet was a prime example, as aesthetics or self-transformative practice. In this paper I take up Foucault's argument with respect to the Animal Liberation Movement and the dietetics of ethical vegetarianism. Contra Foucault, I suggest that diet has not been replaced by sexuality in the modern West, and that food choices, along with and intertwined with sexuality, continue to function as practices of self-constitution in both disciplinary and aesthetic fashions. I then consider the implications of this argument for the Animal Liberation Movement, exploring ways in which it might (and to some degree already does) take on aesthetic rather than moral strategies in order to pursue what Foucault once described as “an ethics of acts and their pleasures which would be able to take into account the pleasure of the other.”

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 imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.072
Scholarly communication0.0040.004
Open science0.0010.003
Research integrity0.0070.005
Insufficient payload (model declined to judge)0.0020.000

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 head0.077
GPT teacher head0.423
Teacher spread0.346 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations40
Published2010
Admission routes1
Has abstractyes

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