“This is real beauty”: pushing the boundaries of aesthetic citizenship online
Bibliographic record
Abstract
Despite recent efforts toward inclusion within the legacy media circuit, body diversity remains incredibly uncommon. This is partly a function of industry conventions that standardize appearance to mitigate against risk inherent in cultural production. In contrast, social media are described as “democratizing” beauty and promoting diversity. But these media platforms still play a role in constraining boundaries around aesthetic citizenship—a status conferred based on appearance. We use aesthetic citizenship to inform an analysis of 300 online images and advertisements posted by three beauty retailers: Benefit, Sephora, and Dove. We find that representations of disability remain rare even while other kinds of representation along the lines of race, for example, are on the rise. We also note that people who embody multiple dimensions of difference are among the most likely to be excluded from images and advertisements of beauty online. Beauty is connected to boundary work and these findings highlight the relationship between everyday representations of beauty and the reproduction of inequality.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.019 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".