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Record W1606165518 · doi:10.1002/mar.20814

Can Hand Washing Influence Hedonic Food Consumption?

2015· article· en· W1606165518 on OpenAlexaff
Chrissy M. Martins, Lauren Block, Darren W. Dahl

Bibliographic record

VenuePsychology and Marketing · 2015
Typearticle
Languageen
FieldNeuroscience
TopicPsychology of Moral and Emotional Judgment
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsConsumption (sociology)PsychologyLicenseSocial psychologyAdvertisingBusiness

Abstract

fetched live from OpenAlex

ABSTRACT The act of hand washing has been a routine part of hygienic practices across time and society. Aside from its physiological effects, hand washing has also been shown to symbolically cleanse individuals of their transgressions. However, most research demonstrating the metaphorical effect of hand washing has mainly been focused within the domain of morality. The objective of the current research is to explore the role of hand washing on consumption behavior, and more specifically, hedonic food consumption. Across two studies, this article establishes the role of hand washing after hedonic food consumption as well as prior to a hedonic food choice. In Study 1, washing one's hands after consumption of hedonic food decreased perceived guilt. In Study 2, washing one's hands prior to choice led to an increased likelihood of choosing a more hedonic (compared to less hedonic) food item. Thus, by washing one's hands, individuals are provided with a license to indulge in hedonic foods without the guilt that is commonly associated with such a choice. Several implications for consumer behavior research and the effects of hand washing on food decision making are discussed.

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)0.001
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.136
GPT teacher head0.327
Teacher spread0.191 · 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 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

Citations12
Published2015
Admission routes1
Has abstractyes

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