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Record W4210772560 · doi:10.3389/fcomm.2021.796425

Discursive Power: Trade Over Health in CARICOM Food Labelling Policy

2022· article· en· W4210772560 on OpenAlexafffund
Lucy Hinton

Bibliographic record

VenueFrontiers in Communication · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Public Health Policies and Epidemiology
Canadian institutionsBalsillie School of International AffairsUniversity of Waterloo
FundersUniversity of WaterlooSocial Sciences and Humanities Research Council of CanadaInternational Development Research Centre
KeywordsFraming (construction)Corporate governanceBusinessPolitical sciencePrivilege (computing)Fair tradeFood safetyNarrativePublic relationsInternational tradeLawEngineering

Abstract

fetched live from OpenAlex

Moving towards a more sustainable, healthier, and equitable food future requires a significant system transformation. Policies to achieve this transformation are notoriously difficult to achieve, especially where actors with conflicts of interest are involved in governance. In this paper, I analyze how corporate actors frame issues inside a process to develop Front-of-Pack Labelling across the Caribbean. Focusing on three major framing strategies, I show how industry actors argued 1) (falsely) that FOPL would privilege Chilean food suppliers; 2) that FOPL would constitute a major transgression of international trade law; and 3) that a regional public health organization (the Pan-American Health Organization) is an illegitimate influence on the policy. Together, these three framing strategies reconstruct the policy problem as one of trade rather than public health. I argue that the resulting narrative is both a product and a function of the discursive power food companies wield in the standard-setting process and provide empirical detail about how food companies act to prevent policy attempts facilitating food systems transformation.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.811
Threshold uncertainty score0.974

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.023
GPT teacher head0.295
Teacher spread0.272 · 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 designNot applicable
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

Citations5
Published2022
Admission routes2
Has abstractyes

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