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Record W3098528084 · doi:10.1080/00224499.2020.1841723

The Influence of Body Shape on Impressions of Sexual Traits

2020· article· en· W3098528084 on OpenAlexaff
Flora Oswald, Amanda Champion, Cory L. Pedersen

Bibliographic record

VenueThe Journal of Sex Research · 2020
Typearticle
Languageen
FieldPsychology
TopicEvolutionary Psychology and Human Behavior
Canadian institutionsKwantlen Polytechnic UniversitySimon Fraser University
Fundersnot available
KeywordsBody shapeHuman sexualityPsychologyTraitBig Five personality traitsSexual attractionPersonalityAttractivenessSocial psychologyAttributionDevelopmental psychologySexual behaviorMedicineGender studies

Abstract

fetched live from OpenAlex

The assumptions people make from body shape can have serious implications for the well-being of the individuals inhabiting such bodies. Fat people are subject to pervasive and resilient social stigma and discrimination, leading to negative mental and physical health outcomes, including negative sexuality-related outcomes. Though previous studies have examined the personality traits attributed to, or the sexual attractiveness of, varying body shapes, no research has asked participants to make attributions of sexual traits to varying body shapes. The purpose of this study was thus to examine sexuality-related trait inferences made from body shapes. Participants (N = 891, 70% women, Mage = 25.28) were randomly assigned to view 5 computer-generated 3-dimensional body models of varying shapes developed using the skinned multi-person linear model. Participants rated their sexual attraction to each body and the degree to which each of 30 traits (10 personality and 20 sexual) applied. Results demonstrated that larger bodies are generally viewed as less sexually attractive. Further, constellations of sexuality traits were predicted reliably by body shape, demonstrating that people hold sexual stereotypes about a diverse range of body shapes. This study provides an initial comprehensive demonstration of the sexuality-specific traits associated with varying body shapes.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.964
Threshold uncertainty score0.878

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.174
GPT teacher head0.473
Teacher spread0.299 · 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

Citations11
Published2020
Admission routes1
Has abstractyes

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