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Record W2891261755 · doi:10.1177/1050651918798683

Digesting Data: Tracing the Chromosomal Imprint of Scientific Evidence Through the Development and Use of Canadian Dietary Guidelines

2018· article· en· W2891261755 on OpenAlexaffabout
Christen Rachul

Bibliographic record

VenueJournal of Business and Technical Communication · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsIdeologyRhetorical questionLimitingResource (disambiguation)Antecedent (behavioral psychology)Public relationsScientific evidenceQualitative researchPsychologySociologySocial psychologyPolitical scienceSocial scienceEngineeringComputer scienceEpistemologyLinguisticsLaw

Abstract

fetched live from OpenAlex

The Eating Well With Canada’s Food Guide (CFG), which represents Canada’s official dietary guidelines, is designed to address high rates of obesity and diet-related chronic disease in Canada. This article presents a qualitative study of the social and ideological actions that the CFG performs. The study draws on the concepts of antecedent genres and uptake from rhetorical genre studies, applying them in a multimodal analysis of the CFG and interviews with the CFG’s producers and registered dietitians (RDs) who work with vulnerable populations. Findings reveal that scientific representations play a profound role in the social and ideological actions that the CFG performs. The author demonstrates how representations of scientific evidence from nutrition science, as exemplified in the concept of the Food Guide Serving, are taken up by the CFG and, in turn, how these scientific representations influence RDs’ use of the CFG and dominate, rather than facilitate, discussions about healthy eating. The study suggests that the CFG, instead of being an enabling resource, is a limiting document: It limits who can make healthier food choices and how such choices can be made.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.572
Threshold uncertainty score0.991

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 studies0.0010.002
Scholarly communication0.0000.001
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 head0.314
GPT teacher head0.359
Teacher spread0.046 · 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 designOther design
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
Published2018
Admission routes2
Has abstractyes

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