“I want to look as if I am my child’s big sister”: Self-satisfaction and the yummy mummy in Taiwan
Bibliographic record
Abstract
This paper explores the trend of stay-fit maternity in Taiwan and extends the feminist analysis of the yummy mummy under neoliberalism to a non-Western context. Drawing insight from Foucault’s critique of the theory of human capital and his emphasis on “psychic return,” it examines a process of continuous interaction between outer appearance and the inner world of these Taiwanese women during and after pregnancy. Thus, by using the perspective of the flux of psychic return in order to understand these women’s continuous aesthetic labour, I emphasize the importance of self-satisfaction as a determinant gain of the valorization of appearance in this process of maximizing self-appreciation and diminishing self-depreciation. I underline not only the importance of the functioning of an economy of affects which supports and overdetermines their beauty practices but also, in some circumstances, that the immaterial return in the quest for beauty takes priority over material earnings and the influences of social pressures. As well, my analysis finds complex and overlapping relations between self-satisfaction and neoliberal rationality such that self-appreciation constitutes the pleasure of embodying a recognized ideal of the maternal, the joy of overcoming undisciplined flesh, and the confidence-enhancement of being mistakenly seen as a young girl.
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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 | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| 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".