Muscles, Makeup, and Femboys: Analyzing TikTok’s “Radical” Masculinities
Bibliographic record
Abstract
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".