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Record W2165004906 · doi:10.1017/s0144686x0700623x

Becoming and being gendered through the body: older women, their mothers and body image

2007· article· en· W2165004906 on OpenAlexaff
Laura Hurd Clarke, Meridith Griffin

Bibliographic record

VenueAgeing and Society · 2007
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBeautyFemininityContext (archaeology)Agency (philosophy)Gender studiesPsychologyFeminismSocial psychologyIdeal (ethics)Doing genderDevelopmental psychologyDaughterSociologyAestheticsPolitical scienceSocial science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.472
Threshold uncertainty score0.437

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.300
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations49
Published2007
Admission routes1
Has abstractyes

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