Standards of Femininity in Traditional and Contemporary China: Stereotypes and Beliefs on Women’s Appearance, Roles and their Cultural Influences
Bibliographic record
Abstract
Technological advancements have advanced patriarchal society. Modern society is bombarded with media, which introduces new styles and trends daily. These new fashion standards often have negative effects and reinforce social norms and stereotypes. Female roles and fashion trends are stereotyped worldwide, but they are rarely rigid. However, many in Eastern Asia, especially China, are concerned about this issue because cultural and media influences shape many stereotypes and perceptions about women’s physical appearances and social roles. People used to embrace their own beauty standards. However, modern society has adopted a standardised ideal, leading many to undergo extensive plastic surgery procedures like nose bridge enhancement, V-shaped face, skin lightening, and double eyelids. It seems like everyone is identical and can be easily replicated. Without inner beauty and values, outer beauty is just a decorative vase. Beauty, like anything else, is harmful in excess. Self-improvement and personal values should be prioritised before cosmetic enhancement. Through impact of social media, Chinese people set beauty standards and keep them for female generations .
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".