Standards of Beauty: The Impact of Mannequins in the Retail Context
Bibliographic record
Abstract
Abstract Across six studies, a female mannequin is demonstrated to have negative implications for both male and female consumers low in appearance self-esteem. In particular, consumers who are lower in appearance self-esteem evaluate a product displayed by a mannequin more negatively as compared with consumers higher in appearance self-esteem. As mannequins signal the normative standard of beauty and consumers with low self-esteem in regard to their appearance believe they fail to meet this standard, these consumers become threatened by the beauty standard when exposed to a mannequin and in response denigrate the product the mannequin is displaying. We provide evidence for the underlying process in three ways: 1) through the finding that the effect for male and female consumers with low appearance self-esteem arises only when the mannequin is displaying an appearance-related product, 2) through mediation analysis demonstrating that the mannequin conveys society’s standard of beauty and that this negatively impacts product evaluations, and 3) through mitigation of the effect by removing the presence of threat via a self-affirmation task or decreasing the mannequin’s beauty (e.g., marking its face, removing its hair, or removing its head). Multiple avenues for future research are forwarded.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".