<i>Nennu</i>and<i>Shunu</i>: Gender, Body Politics, and the Beauty Economy in China
Bibliographic record
Abstract
This essay analyzes recent discourse on two emerging representations of women in China, "tender" women (nennu) and "ripe" women (shunu), in order to examine the relationships among gender, body politics, and consumerism. The discourse of nennu and shunu suggests that older, ripe women become younger and more tender by consuming fashions, cosmetic surgery technologies, and beauty and health care products and services because tender women represent the ideal active consumership that celebrates beauty, sexuality, and individuality. This discourse serves to enhance consumers' desire for beauty and health and to ensure the continued growth of China's beauty economy and consumer capitalism. Highlighting the role of the female body, feminine beauty, and feminine youth in developing consumerism, this discourse downplays the contributions of millions of beauty and health care providers (predominantly laid-off female workers and rural migrant women) and new forms of gender exploitation. Such an overemphasis on gender masks intensified class division. This essay suggests that women and their bodies become new terrains from which post-Mao China can draw its power and enact consumerism. Gender constitutes both an economic multiplier to boost China's consumer capitalism and a biopolitical strategy to regulate and remold women and their bodies into subjects that are identified with the state's political and economic objectives. Since consumerism has been incorporated into China's nation-building project, gender thus becomes a vital resource for both consumer capitalist development and nation building. This essay shows that both gender and the body are useful analytic categories for the study of postsocialism.
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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 | 0.005 | 0.006 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".