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Record W4293175794 · doi:10.1080/00224499.2022.2112647

Gayzing Women’s Bodies: Criticisms of Labia Depend on the Gender and Sexual Orientation of Perceivers

2022· article· en· W4293175794 on OpenAlexaff
Flora Oswald, Cory L. Pedersen, Jes L. Matsick

Bibliographic record

VenueThe Journal of Sex Research · 2022
Typearticle
Languageen
FieldMedicine
TopicFemale Genital Mutilation/Cutting Issues
Canadian institutionsKwantlen Polytechnic University
Fundersnot available
KeywordsLabiaLesbianPsychologySexual orientationSocial psychologyPerceptionDevelopmental psychologyDisgustClinical psychologyMedicineAnger

Abstract

fetched live from OpenAlex

The heterosexual male gaze is often credited with producing bodily anxieties among women, yet empirical and popular cultural evidence suggest gay men have especially negative views toward women’s bodies, particularly women’s genitalia. Across two studies (N = 6,129; Mage = 27.58; 2,047 women, 4,082 men) we conducted secondary analyses of existing datasets to test the hypotheses that gay men would evaluate labia more negatively than heterosexual men, and that lesbian women would evaluate labia more positively than heterosexual women. We conducted fixed-effects mini meta-analyses to estimate summary effect sizes for perceptions of normalcy and fit with societal ideals; we additionally assessed an outcome of disgust in Study 2. We found support for our hypotheses: For normalcy and societal ideal, we found small summary effects such that gay men evaluated labia more negatively than heterosexual men, and medium summary effects such that lesbian women evaluated labia more positively than heterosexual women. Gay men also rated labia as more disgusting than any other demographic group, and lesbian women rated the stimuli as less disgusting than heterosexual women, supporting our hypotheses. The current findings suggest a pressing need to acknowledge and incorporate gay men’s perceptions of women’s bodies into literatures on misogyny, objectification, and body image more generally.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.303
Threshold uncertainty score0.473

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
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 integrity0.0000.001
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 head0.146
GPT teacher head0.401
Teacher spread0.255 · 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 designQualitative
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

Citations4
Published2022
Admission routes1
Has abstractyes

Explore more

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