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Record W2067747683 · doi:10.1177/0963662511412861

The weight of communication: The <i>Canadian Medical Association Journal</i>’s discourse on obesity

2011· article· en· W2067747683 on OpenAlexafffundabout
Angela Eileen Wisniewski

Bibliographic record

VenuePublic Understanding of Science · 2011
Typearticle
Languageen
FieldHealth Professions
TopicObesity and Health Practices
Canadian institutionsUniversity of New Brunswick
FundersCanadian Medical Association
KeywordsIdeologyRhetoricPoliticsContext (archaeology)ObesityAssociation (psychology)Field (mathematics)Public relationsExploratory researchPolitical scienceSociologySocial scienceMedia studiesPsychologyMedicineLawHistoryPathology

Abstract

fetched live from OpenAlex

In this exploratory analysis, I use a Burkean dramatist approach to investigate the relatively under-examined dynamics of how medical knowledge on obesity has changed outside of the American context. I examine how, over the past forty years, Canadian medical professionals have used the Canadian Medical Association Journal to generate a field of knowledge which organizes the ways in which obesity can be described, studied and treated. I argue that since the 1970s medical professionals have been increasingly interested in the relationship between obesity and a broadly defined social environment, and that this merger is rhetorically realized in the concept of the "obesogenic environment." I suggest that the process of engaging obesity has generated rhetoric that has often been resonant with the political ideologies expressed in health policy, but that can also create opportunities for the expression of alternative social goals.

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)0.013
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.941
Threshold uncertainty score0.668

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.026
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0080.011
Science and technology studies0.0590.064
Scholarly communication0.0260.008
Open science0.0030.010
Research integrity0.0070.011
Insufficient payload (model declined to judge)0.0050.000

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 head0.209
GPT teacher head0.428
Teacher spread0.219 · 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.

Study designQualitative
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

Citations4
Published2011
Admission routes3
Has abstractyes

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