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Record W2411377694

Pleasure As an Ally of Healthy Eating? Contrasting Visceral and Epicurean Eating Pleasure and Their Association With Portion Size Preferences and Wellbeing

2015· article· en· W2411377694 on OpenAlexaff
Yann Cornil, Pierre Chandon

Bibliographic record

VenueACR North American Advances · 2015
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPleasureEpicureanismPsychologyOvereatingSocial psychologyEmotional eatingAestheticsEating behaviorMedicineObesityPsychotherapistLiteratureArt
DOInot available

Abstract

fetched live from OpenAlex

Research on overeating and self-regulation has associated eating pleasure with short-term visceral impulses triggered by hunger, external cues, or internal emotional urges. Drawing on research on the social and cultural dimensions of eating, we contrast this approach with what we call eating pleasure, which is the enduring pleasure derived from the aesthetic appreciation of the sensory and symbolic value of the food. To contrast both approaches, we develop and test a scale measuring Epicurean eating pleasure tendencies and show that they are distinct from the tendency to experience visceral pleasure (measured using the external eating and emotional eating scales). We find that Epicurean eating pleasure is more prevalent among women than men but is independent of age, income and education. Unlike visceral eating pleasure tendencies, Epicurean eating tendencies are associated with a preference for smaller food portions and higher wellbeing, and not associated with higher BMI. Overall, we argue that the moralizing approach equating the pleasure of eating with 'low-level' visceral urges should give way to a more holistic approach which recognizes the positive role of Epicurean eating pleasure in healthy eating and wellbeing.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.113
Threshold uncertainty score0.674

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 studies0.0000.000
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 head0.015
GPT teacher head0.294
Teacher spread0.278 · 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 designObservational
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

Citations0
Published2015
Admission routes1
Has abstractyes

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