Real versus ideal: How selfies drive young women’s endorsement of beauty ideals to enhance cosmetic surgery intentions
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.
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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 | 0.001 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".