Perceptions of Female Genitalia Following Labiaplasty
Bibliographic record
Abstract
Labiaplasty – a common form of female genital cosmetic surgery involving the removal of portions of the labia minora – is becoming increasingly popular, yet little research has examined perceptions of postoperative labia relative to perceptions of unaltered labia. The purpose of this study was therefore to examine perceptions of preoperative and postoperative labia. A sample of 4513 participants – 42% women, 56% men, and 3% non-binary (Mage 27.01, SDage = 9.97) – was shown a randomized series of “before-and-after” images of labiaplasty procedures. Participants rated each image on how well it matched societal ideals, their personal ideal, and perceived normalcy in appearance. Our hypothesis that postoperative labia would be evaluated more favorably than preoperative labia on these constructs was supported. Individuals who specified their gender outside of the binary rated labia more positively overall; women rated labia more negatively than participants of other genders. Ratings were consistently low overall for both pre- and postoperative labia, suggesting critically negative perceptions of female genitalia. Our findings highlight a need for interventions and education to encourage more positive and accurate views of women’s bodies.
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 | 0.002 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".