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‘Taking charge of your health’: discourses of responsibility in English‐Canadian women's magazines

2008· article· en· W1601282185 on OpenAlexaffabout
Stephannie C. Roy

Bibliographic record

VenueSociology of Health & Illness · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Feminism, and Media
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAction (physics)Subject (documents)Gender studiesMoral responsibilityPosition (finance)SociologyDiscourse analysisMedia studiesPolitical scienceLawLinguistics

Abstract

fetched live from OpenAlex

This article presents an examination of the ways in which responsibility for health is constructed in popular English-Canadian women's magazines. Women's magazines are a unique media form, acting as guidebooks for women on matters relating to feminine gender roles and are important to examine as part of the corpus of societal discourses which frame our understandings of what it means to be healthy and how good health is achieved. Using discourse analysis several techniques were found which reinforce women's individual responsibility to create and maintain good health for themselves and their families. The magazines instruct women/readers directly about their health-related responsibilities and outline the negative consequences of inaction or incorrect action. The magazines also use the traditional discursive technique of women's personal accounts as both cautionary tales and inspirational stories to encourage readers to actively pursue healthy behaviours. Reflecting and reinforcing the discourse of healthism, women's magazines consistently present health as an important individual responsibility and a moral imperative which creates an entrepreneurial subject position for women. The article concludes by discussing the implications for women's magazine audiences within the ongoing feminist debate about this cultural industry.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.101
Threshold uncertainty score0.604

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.018
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0050.004
Science and technology studies0.0390.041
Scholarly communication0.0150.006
Open science0.0020.007
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.065
GPT teacher head0.376
Teacher spread0.311 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations96
Published2008
Admission routes2
Has abstractyes

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