Visible and invisible ageing: beauty work as a response to ageism
Bibliographic record
Abstract
ABSTRACT This paper examines how older women experience and respond to ageism in relation to their changing physical appearances and within the context of their personal relationships and places of employment. We elucidate the two definitions of ageism that emerged in in-depth interviews with 44 women aged 50 to 70 years: the social obsession with youthfulness and discrimination against older adults. We examine the women's arguments that their ageing appearances were pivotal to their experience of ageism and underscored their engagement in beauty work such as hair dye, make-up, cosmetic surgery, and non-surgical cosmetic procedures. The women suggested that they engaged in beauty work for the following underlying motivations: the fight against invisibility, a life-long investment in appearance, the desire to attract or retain a romantic partner, and employment related-ageism. We contend that the women's experiences highlight a tension between being physically and socially visible by virtue of looking youthful, and the realities of growing older. In other words, social invisibility arises from the acquisition of visible signs of ageing and compels women to make their chronological ages imperceptible through the use of beauty work. The study extends the research and theorising on gendered ageism and provides an example of how women's experiences of ageing and ageism are deeply rooted in their appearances and in the ageist, sexist perceptions of older women's bodies.
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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 | 0.003 | 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 | 0.004 | 0.010 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.005 |
| 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".