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Record W3214606373

Eating as a Self-Shaping Activity: The Case of Young Women’s Vegetarianism and Eating Disorders

2021· article· en· W3214606373 on OpenAlexvenueno aff
Megan A. Dean

Bibliographic record

VenueFeminist Philosophy Quarterly · 2021
Typearticle
Languageen
FieldNeuroscience
TopicPsychology of Moral and Emotional Judgment
Canadian institutionsnot available
Fundersnot available
KeywordsEating disordersMistakePsychologyAgency (philosophy)Social psychologyIntervention (counseling)Disordered eatingSign (mathematics)Relevance (law)Developmental psychologyEpistemologyClinical psychologyPsychiatryPolitical sciencePhilosophy
DOInot available

Abstract

fetched live from OpenAlex

This paper contends that eating shapes the self; that is, our practices and understandings of eating can cultivate, reinforce, or diminish important aspects of the self, including agency, values, capacities, affects, and self-understandings. I argue that these self-shaping effects should be included in our ethical analyses and evaluations of eating. I make a case for this claim through an analysis and critique of the hypothesis that young women’s vegetarianism is a risk, sign, or “cover” for eating disorders or disordered eating. After outlining the relevant empirical literature, I suggest that the evidence for this hypothesis is inconclusive. Given this uncertainty, we should consider the risks of making a mistake when accepting or rejecting this understanding of young women’s eating. I argue that these risks importantly include negative effects on the self, such as damage to moral and epistemic agency. Along with other potential consequences of mistakenly accepting the hypothesis, these effects give us reason to reject it pending more conclusive research. Overall, this paper offers a philosophical intervention into the debate over the relationship between vegetarianism and eating disorders while illustrating the ethical importance and relevance of eating as a self-shaping activity.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.634
Threshold uncertainty score0.646

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.050
GPT teacher head0.273
Teacher spread0.223 · 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 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

Citations2
Published2021
Admission routes1
Has abstractyes

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