Shades of grey: to dye or not to dye one's hair in later life
Bibliographic record
Abstract
ABSTRACT This article examines older women's perceptions of grey, white and coloured hair. Using data from in-depth interviews with 36 women aged 71–94 years (mean 79), we elucidate the women's attitudes towards and reasons for dyeing or not dyeing their hair. The majority of our participants disparaged the appearance of grey hair, which they equated with ugliness, dependence, poor health, social disengagement and cultural invisibility. The women were particularly averse to their own grey hair, and many suggested that other women's grey hair was acceptable, if not attractive. At the same time, half of the women liked the look of snowy white hair, which they associated with attractiveness in later life as well as with goodness and purity. While one-third of the women had begun to dye their hair in their youth so as to appear more fashionable, two-thirds continued to dye their hair in later life so as to mask their grey hair and their chronological age. The women suggested that they used hair dye to appear more youthful and to resist ageist stereotypes associated with older women. We discuss the findings in relation to previous research concerning older women's hair, the concept of doing gender, and theories pertaining to ageism.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 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".