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Record W4297137710 · doi:10.1177/20563051221126040

Muscles, Makeup, and Femboys: Analyzing TikTok’s “Radical” Masculinities

2022· article· en· W4297137710 on OpenAlex
Jordan Foster, Jayne Baker

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSocial Media + Society · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Feminism, and Media
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMasculinityMainstreamHuman sexualityHegemonic masculinityBeautySociologyGender studiesAestheticsConformityAttractivenessMale gazeRace (biology)PsychologySocial psychologyArtPolitical science

Abstract

fetched live from OpenAlex

News reports and online comments suggest that social media applications like TikTok play an important role in challenging traditional notions of masculinity. Male creators who don jewelry and engage in dance in their videos are emblematic of a broader shift in social and mainstream media toward gender non-conformity. Do these videos represent a movement away from hegemonic ideals? Based on a visual content analysis of 205 TikTok videos across the application’s 43 most followed male creators, we examine representations of masculinity on the platform. Drawing on the concept of hybrid masculinity, we find that TikTok creators both challenge and reinforce traditional notions of masculinity, subverting widely recognizable tropes, and gender norms while simultaneously reinforcing the importance of men’s muscularity, attractiveness, and sexual bravado. Taken together, our findings contribute to a broader discussion of the role that social media play in reproducing inequality along the lines of gender, race, and sexuality, including how beauty is rewarded symbolically and materially in online spaces.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.187
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.047
GPT teacher head0.289
Teacher spread0.242 · 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