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Record W2763907374 · doi:10.1080/14461242.2017.1383856

Self-responsibility, fatality, and heroism: a discourse analysis of ovarian cancer in women’s magazines

2017· article· en· W2763907374 on OpenAlexaffabout
Meridith Burles

Bibliographic record

VenueHealth Sociology Review · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Feminism, and Media
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsSociologyProject commissioningGender studiesDiscourse analysisOvarian cancerMedia studiesPublishingCancerPolitical scienceMedicineLawInternal medicineLinguisticsPhilosophy

Abstract

fetched live from OpenAlex

Ovarian cancer affects many women globally, having numerous physical and psychosocial implications. However, misconceptions abound, symptoms are often overlooked, and diagnosis frequently occurs in advanced stages. As one step to addressing these issues, this research explores the social construction of ovarian cancer in women's magazines to identify the ideas and discourses surrounding this illness and interpret their explicit and implicit meanings. A constructivist discourse analytic approach guided analysis of 62 print and online articles from 8 women's magazines available in Canada over a 20-year period. Analysis resulted in identification of three discourses pertaining to: self-responsibility for health, ovarian cancer as uncertain and inevitably fatal, and ovarian cancer as a heroic endeavour. Critical interpretation highlights misinformation, contradictory beliefs, and unrealistic expectations surrounding this illness, which have implications for healthy and affected women. The findings emphasise the importance of identifying and challenging these discursive constructions to expose inconsistencies, minimise harm to women's well-being, and promote authentic portrayals of ovarian cancer.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.400
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.075
GPT teacher head0.473
Teacher spread0.398 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations5
Published2017
Admission routes2
Has abstractyes

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