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Record W2939112106 · doi:10.1177/0146167219838550

Shaping the Body Politic: Mass Media Fat-Shaming Affects Implicit Anti-Fat Attitudes

2019· article· en· W2939112106 on OpenAlexafffund
Amanda Ravary, Mark W. Baldwin, Jennifer A. Bartz

Bibliographic record

VenuePersonality and Social Psychology Bulletin · 2019
Typearticle
Languageen
FieldHealth Professions
TopicObesity and Health Practices
Canadian institutionsMcGill University
FundersDivision of Materials ResearchFonds de Recherche du Québec-Société et Culture
KeywordsImplicit attitudePsychologySocial psychologyBody politicPopulationPsycheSalientSociologyPoliticsPolitical science

Abstract

fetched live from OpenAlex

The human psyche is profoundly shaped by its cultural milieu; however, few studies have examined the dynamics of cultural influence in everyday life, especially when it comes to shaping people’s automatic, implicit attitudes. In this quasi-experimental field study, we investigated the effect of transient, but salient, cultural messages—the pop-cultural phenomenon of celebrity “fat-shaming”—on implicit anti-fat attitudes in the population. Adopting the “copycat suicide” methodology, we identified 20 fat-shaming events in the media; next, we obtained data from Project Implicit of participants who had completed the Weight Implicit Association Test from 2004 to 2015. As predicted, fat-shaming led to a spike in women’s (N=93,239) implicit anti-fat attitudes, with events of greater notoriety producing greater spikes. We also observed a general increase in implicit anti-fat attitudes over time. Although these passing comments may appear harmless, we show that feedback at the cultural level can be registered by the “body politic.”

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.118
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.002

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 head0.105
GPT teacher head0.470
Teacher spread0.365 · 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; both teacher heads agree on what is shown here.

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

Citations75
Published2019
Admission routes2
Has abstractyes

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