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Record W2344028549 · doi:10.1386/csmf.3.1.17_1

Destabilizing the gaze towards male fashion models: Expanding men’s gender and sexuality identities

2016· article· en· W2344028549 on OpenAlexafffund
Ben Barry, Barbara J. Phillips

Bibliographic record

VenueCritical Studies in Men’s Fashion · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicFashion and Cultural Textiles
Canadian institutionsUniversity of SaskatchewanToronto Metropolitan University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsHuman sexualityObjectificationGazeMale gazePsychologyNormativeMainstreamGender studiesFantasyConformityMasculinitySexualizationSocial psychologyEmpirical researchDevelopmental psychologySociologyPsychoanalysisArt

Abstract

fetched live from OpenAlex

Abstract Fashion advertisements pioneered the mainstream objectification of the male body in popular culture. While scholars have theorized about the influence of these images on men, few empirical studies have examined men’s engagement with them. This study investigates how men experience objectified men’s fashion advertisements through interviews with 30 gay and straight male fashion consumers. Analysis revealed that both gay and straight men gaze upon images through the lenses of appreciation and fantasy, destabilizing normative binaries of gender and sexuality. Despite the delight that men experienced, objectified fashion images roused despair and distress because the models represented limited body ideals and expressions of gender and sexuality. Findings provide empirical evidence to support and advance a new theorization of the male gaze in men’s fashion images. Fashion professionals are advised to expand their representations of male imagery to enable men to continue to traverse gender and sexuality boundaries.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0040.007
Scholarly communication0.0040.002
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.206
GPT teacher head0.362
Teacher spread0.156 · 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 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

Citations9
Published2016
Admission routes2
Has abstractyes

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