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Record W2914354554 · doi:10.3390/nu11020268

Global Governance of Front-of-Pack Nutrition Labelling: A Qualitative Analysis

2019· article· en· W2914354554 on OpenAlexaff
Anne Marie Thow, Alexandra Jones, Carmen Huckel Schneider, Ronald Labonté

Bibliographic record

VenueNutrients · 2019
Typearticle
Languageen
FieldMedicine
TopicConsumer Attitudes and Food Labeling
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsLabellingCorporate governanceGovernment (linguistics)Public policyPublic healthQualitative researchWork (physics)Political sciencePublic relationsGlobal governanceBusinessSociologyMedicinePathology

Abstract

fetched live from OpenAlex

The Codex Alimentarius has approved ongoing work for international guidance on front-of-pack (FoP) nutrition labelling, which is a core intervention for prevention of diet-related noncommunicable disease. This guidance will have implications for national policy decision-making regarding this important public health issue. However, FoP nutrition labelling is also a trade and commerce policy issue. In this study, we analyze the global governance of FoP nutrition labelling and current policy processes, to inform public health policy and advocacy. We present findings from a qualitative governance and institutional analysis, based on key informant interviews with 28 global actors. The study found that Codex guidance was perceived as likely to have a high impact on FoP nutrition labelling globally. However, a small and highly interconnected "regime complex" of international institutions surrounds FoP nutrition labelling at the global level, and influence on Codex discussions is being exerted differentially by actors at the national and global level, particularly by government and industry actors. There are thus risks associated with conflicts of interests in the development of global guidance on FoP nutrition labelling. There are also opportunities for more strategic and coordinated public health engagement.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.088
Threshold uncertainty score0.334

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.024
GPT teacher head0.346
Teacher spread0.322 · 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

Citations64
Published2019
Admission routes1
Has abstractyes

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