“This is real beauty”: pushing the boundaries of aesthetic citizenship online
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.
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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it