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Record W2286375326 · doi:10.2304/pfie.2014.12.7.945

The Individualization of Risk and Responsibility in Breast Cancer Prevention Education Campaigns

2014· article· en· W2286375326 on OpenAlexaffabout
Ellen Sweeney

Bibliographic record

VenuePolicy Futures in Education · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicEffects and risks of endocrine disrupting chemicals
Canadian institutionsYork University
Fundersnot available
KeywordsBreast cancerMainstreamGovernment (linguistics)Environmental healthConsumption (sociology)Health educationPoliticsPublic healthPromotion (chess)Public relationsEconomic growthPolitical scienceCancerMedicineSociologyHealth careEconomicsSocial scienceLaw

Abstract

fetched live from OpenAlex

Breast cancer is the most commonly diagnosed cancer in women worldwide. The incidence rates are such that one in nine Canadian women will be diagnosed in her lifetime. While social science research has demonstrated the influence of social, political, economic and environmental factors on health outcomes, many still emphasize the role of traditional risk factors for breast cancer, such as family history or diet. However, these factors are unable to account for the increased incidence of the disease in industrialized countries. Thus, there is a call for more attention to environmental links to breast cancer, and as a result, it has become necessary to consider the ‘everyday exposures' that we experience in our daily lives, which often include mammary carcinogens and endocrine disrupting chemicals through exposure to industrial chemicals and toxic substances in consumer products. In this article, the author explores and critiques two breast cancer education campaigns which are promoted by the Canadian federal government and by mainstream breast cancer organizations. Both the responsibilization paradigm and the promotion of precautionary consumption practices engage with issues of risk and responsibility at the level of the individual. This focus on modifiable behaviours and lifestyle factors is highly problematic as it does not adequately account for other determinants of health, particularly those outside one's control, such as environmental contaminants. Only a truly precautionary approach can be effective in protecting women's health.

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)0.001
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 score0.277
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.005
GPT teacher head0.370
Teacher spread0.365 · 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

Citations7
Published2014
Admission routes2
Has abstractyes

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