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Record W1547636769 · doi:10.22230/cjc.2007v32n3a1724

Spreading the News: Social Determinants of Health Reportage in Canadian Daily Newspapers

2007· article· en· W1547636769 on OpenAlexaffvenueabout
Mike Gasher, M A Hayes, Robert A. Hackett, Donald Gutstein, I. Ross, James R. Dunn

Bibliographic record

VenueCanadian Journal of Communication · 2007
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Public Health Policies and Epidemiology
Canadian institutionsUniversity of TorontoGovernment of OntarioSimon Fraser UniversityConcordia University
Fundersnot available
KeywordsNewspaperJournalismHealth literacyRepresentation (politics)Health carePublic relationsSocial determinants of healthSociologyMedia studiesSocial practiceLiteracySocial mediaHealth communicationPolitical scienceHistoryPedagogyLaw

Abstract

fetched live from OpenAlex

As part of a research program called CHAMP (Canadian Health and Media Project) devoted to examining health literacy in Canadian daily newspapers, and operating from a theoretical framework that posits journalism as a practice of representation, this article is based on a series of formal interviews with English-language and French-language health reporters. The interviews sought answers to three central questions about health reportage: how do journalists demarcate such a vast topic as health? where do they find their stories? and to what extent are they familiar with research into the social determinants of health? It concludes that in spite of their dependence upon published scholarly research as a source of news stories, Canadian health reporters overemphasize the roles of the health care system and personal health habits in the production of Canadians’ health, and they underemphasize the role of social determinants.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.456
Threshold uncertainty score0.342

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.056
GPT teacher head0.339
Teacher spread0.283 · 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

Citations66
Published2007
Admission routes3
Has abstractyes

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