Revealing Narratives in Before and After Photographs of Cosmetic Breast Surgeries
Bibliographic record
Abstract
Feminist cosmetic surgery scholars have been attentive to cosmetic breast surgeries as emblematic of a range of issues and questions. Breast implant and reduction surgeries have been analyzed by scholars as psychologically beneficial, as representative of unethical practices in the cosmetic surgery industry, as exemplar of the objectification of women’s bodies, and as connected to powerful cultural ideas about breasts. A curious dearth in previous scholarship is a sufficient engagement with the ubiquitous library of photographs that document these procedures. This essay discusses before and after photographs of cosmetic breast surgeries, which occupy a liminal space as medical and sexual, verification and fantasy. In this essay, I argue that before and after photographs of cosmetic breast surgeries should be read as revealing of the conditions under which patients and surgeons operate, rather than solely as proof of an operation’s results. To make this argument, I focus on two examples of before and after photographs – one of a breast augmentation and one of a breast reduction – and guide my analysis of the images in relation to narrative interviews with three women who underwent cosmetic breast surgeries.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.017 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".