Older women using women's magazines: the construction of knowledgeable selves
Bibliographic record
Abstract
Abstract Women's magazines are widely read in Canada. The popularity of such magazines is significant because critical gerontologists, primarily drawing on content analyses of the magazines, often argue that these publications convey problematic messages about ageing. This article broaches the subject of women's magazines and ageing from a different vantage point, that of the older woman reader herself. This audience-centred research draws on 21 semi-structured interviews with Canadian women over the age of 55. The study examines what older women say about the ageing-related content of women's magazines, along with what they say about how, when and why they read these magazines. Findings illustrate that participants are aware of the inadequate and unrealistic representations of older women in women's magazines. Nonetheless, they value the publications as a source of practical information on a variety of topics and as a light and undemanding source of entertainment and relaxation. The study reveals how participants assess and deploy magazine contents and characteristics in ways that contribute to, and are informed by, their lives and identities as older women. Against the broader cultural context of ageism, using and talking about women's magazines enables the participants to position themselves as knowledgeable and informed on a variety of topics and in multiple interactions, both in explicit reference to the magazines themselves and more generally in their lives.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".