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Record W2885194731

Communicating Empowerment through Education: Learning about Women’s Health in Chatelaine

2014· article· en· W2885194731 on OpenAlexaboutno aff
Heather McIntosh

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMicrofinance and Financial Inclusion
Canadian institutionsnot available
Fundersnot available
KeywordsEmpowermentComputer scienceWorld Wide WebMultimediaPolitical science
DOInot available

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.438
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.197
GPT teacher head0.510
Teacher spread0.312 · 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 teacher head, not a consensus.

Study designObservational
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

Citations3
Published2014
Admission routes1
Has abstractyes

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