Bat wings, bunions, and turkey wattles: body transgressions and older women's strategic clothing choices
Bibliographic record
Abstract
ABSTRACT This paper examines older women's experiences and perceptions of clothing prescriptions for adults in later life. Using data from in-depth interviews with 36 women aged 71 to 93 years, we investigate the stringent, taken-for-granted social norms that older women identified with respect to appropriate fashion for the ageing female body. Specifically, the participants argued that older women should refrain from wearing bright colours and revealing or overly suggestive styles. Expressing a preference for classic or traditional styles, the women also reported that they used clothing strategically to mask or compensate for bodily transgressions that had occurred over time as a result of the physical realities of ageing, including weight gain, altered body shapes, wrinkles and sagging or ‘flabby’ arms and necks, referred to respectively as ‘bat wings’ and ‘turkey wattles’. In addition, the women contended that they consciously chose their clothing styles to compensate for age-related health issues and/or to present a competent, healthy self to others. Finally, the women talked about the ways in which their clothing choices were influenced by their changing lifestyles and constrained by a lack of desirable and affordable clothing options for the older female body. The findings are discussed in the light of Erving Goffman's concept of stigma and contemporary theorising about ageing, ageism, beauty work and the body.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".