Women's health magazines and postfeminist healthism: A critical discourse analysis
Bibliographic record
Abstract
Daily, millions of North American women turn to popular media, like women's health magazines, for health-related advice and information. “Magazines continue to be a popular medium” and, given their power and reach, remain an important site for critical analysis. In this article, we examine the ways in which three popular women's health magazines provide “health” advice to their readers. We first provide an overview of research in feminist media studies exploring postfeminist sensibility, healthism, and now a postfeminist healthism. We then conduct a critical discourse analysis of articles from all 30 issues of Health, Women's Health, and Shape magazines published in 2018. Our investigation of how the magazines portray what it means to have “good health,” suggests that health continues to be conflated with appearance. Moreover, health is presented as confusing, such that maintaining health requires expert help, and as purchasable, and thus linked to economic practices.
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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 | 0.016 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.014 | 0.028 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".