Real versus ideal: How selfies drive young women’s endorsement of beauty ideals to enhance cosmetic surgery intentions
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
With the prevalence of photo-editing apps, young women nowadays often present ideal but unnatural beauty images in their selfies posted on social networking sites. In view of the possible impact that exposure to the enhanced selfies might have on women’s beauty image concerns, there are campaigns like #Filterdrop and #Nomakeup advocating presenting natural appearance in selfies and promoting acceptance of natural beauty. This study aims to investigate the impact of viewing enhanced (i.e. idealized) selfies, natural (i.e. unaltered, makeup-free, and enhancement-free) selfies, and a mixed set of both (i.e. idealized selfies and natural selfies appear alternately) on young women’s beauty standards and their intentions to alter their appearance. The research involved a between-subjects experiment conducted among 428 young women in the United States. The findings indicate that the more enhanced selfies young women saw, the more they believed others endorsed the cultural beauty ideals. The perceived beauty standards were associated with the young women’s personal beauty standards and their intention to take cosmetic surgery in real life.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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