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Record W2037098730 · doi:10.1080/13698570802536567

‘Tales of mind over cancer’: Cancer risk and prevention in the Canadian print media

2009· article· en· W2037098730 on OpenAlexaffabout
Eva Musso, Sarah Wakefield

Bibliographic record

VenueHealth Risk & Society · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicRisk Perception and Management
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNewspaperOrder (exchange)Cancer preventionMedia coverageControl (management)Public relationsCancerEnvironmental healthPolitical sciencePsychologySociologyMedicineAdvertisingBusinessMedia studiesComputer science

Abstract

fetched live from OpenAlex

This paper investigates coverage of cancer in three Canadian newspapers in order to explore how the print media presents issues related to cancer risk and prevention. Six months of newspaper coverage was coded and analysed quantitatively to identify trends in coverage. A small subset of these articles was then analysed qualitatively to identify latent and underlying messages. Results from the analysis suggest that coverage emphasised risk management through individual choice and lifestyle change, privileging a discourse of individual control. Conversely, social and environmental risks related to cancer were minimised, despite the emerging academic consensus around the central importance of social and environmental determinants of health. Lifestyle was the most frequently mentioned source of cancer risk. Social and environmental health links were mentioned sparingly, and when environmental links were presented they were contested in ways that lifestyle risks were not. The coverage reflects the prevalent discourse in Canadian society that responsibility for health management risk lies primarily with the individual.

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)0.001
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.162
Threshold uncertainty score0.327

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0070.008
Science and technology studies0.0060.003
Scholarly communication0.0050.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.042
GPT teacher head0.402
Teacher spread0.360 · 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 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

Citations28
Published2009
Admission routes2
Has abstractyes

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