Communicating Empowerment through Education: Learning about Women’s Health in Chatelaine
Bibliographic record
Abstract
To understand the ways in which Canadian women’s health knowledge is influenced by media texts, this paper explores the presentation of women’s health in Canada’s longest running women’s magazine, Chatelaine. Reflections on the positioning of women’s bodies in Canadian society are explored to understand the evolution of the discussion of women’s bodies throughout the 20th century. Perspectives on power, the body, and sexuality are traced to understand more recent discussions on women’s health in the Canadian public sphere. Feminist theorizing on the evolution and emergence of the modern female body in Western society is relied upon to obtain contemporary perspectives on women’s bodies and health. To study the ways in which such themes are presented in Chatelaine, a content analysis guided by frame theory is used to examine the ways in which Chatelaine frames information pertaining to women’s health from 1928 to 2010. Findings demonstrate Chatelaine’s growth in women’s health content, as evidenced in the increase in the volume of health content in the magazine and the sophistication and diversification of discussions on women’s bodies and wellness. It is suggested that Chatelaine’s dedication to the coverage of women’s health aids in the empowerment of women, as knowledge about their bodies and wellness is an essential tool necessary for bodily empowerment and female autonomy.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.021 | 0.010 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".