“Selfie”-objectification: effects of sexually objectifying selfies on young women's self-objectification and intention to have cosmetic surgery
Bibliographic record
Abstract
Purpose The ubiquitous edited selfies on social networking sites (SNSs) demonstrate self-objectification, as young women begin to perceive themselves based on how they appear to others while taking, editing and posting their selfies. Our study proposes and tests a model that outlines two pathways through which viewing selfies on SNSs can influence young women's self-objectification and intention to undergo cosmetic surgery. Design/methodology/approach A survey company in China was commissioned to conduct a web-based survey of 604 young Chinese women using its national online panel of adult Internet users. Findings Young women's frequency of viewing sexually objectifying selfies on SNSs was positively associated with their internalization of beauty ideals and their perception that women tend to be objectified in social reality. The internalization and perceived social reality are positively associated with these young women's self-objectification, posting edited selfies and cosmetic surgery intention. Originality/value This study examines how young women's active engagement with edited selfies, including both viewing and posting them on SNSs, reinforces a culture of self-objectification, reshaping the traditional notions of objectification in digital space. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-12-2024-0805
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.008 | 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".