Becoming and being gendered through the body: older women, their mothers and body image
Bibliographic record
Abstract
ABSTRACT Following West and Zimmerman's (1987) theoretical understanding of how gender identities are created and maintained, this paper examines the ways in which older women learned from their mothers how ‘to do gender’ through their bodies and specifically their physical appearances. Extracts from semi-structured interviews with 44 women aged 50 to 70 years have been drawn upon to identify and discuss the ways in which women perceive, manage and present their bodies using socially-constructed ideals of beauty and femininity. More specifically, three ways that women learned ‘to do gender’ are examined: from their mothers' criticisms and compliments about their appearance at different stages of the lifecourse; from their mothers' attitudes towards their own bodies when young and in late adulthood; and from the interviewees' own later-life experiences and choices about ‘beauty work’. Interpretative feminism is employed to analyse how the women exercised agency while constructing body-image meanings in a social context that judges women on their ability to achieve and maintain the prevailing ideal of female beauty. The study extends previous research into the influence of the mother-daughter relationship on young women's body image. The findings suggest that mothers are important influences on their daughters' socialisation into body-image and beauty work, and exert, or are perceived to exert, accountability across the life-course.
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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 | 0.004 |
| 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 | 0.001 |
| Research integrity | 0.000 | 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".