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Record W4210544403 · doi:10.1016/s2214-109x(21)00570-2

Industry influence over global alcohol policies via the World Trade Organization: a qualitative analysis of discussions on alcohol health warning labelling, 2010–19

2022· article· en· W4210544403 on OpenAlexaff

Bibliographic record

VenueThe Lancet Global Health · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Public Health Policies and Epidemiology
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsQualitative analysisGlobal healthQualitative researchWorld tradePublic healthAlcoholQualitative comparative analysisMEDLINE

Abstract

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BACKGROUND: Accelerating progress to implement effective alcohol policies is necessary to achieve multiple targets within the WHO global strategy to reduce the harmful use of alcohol and the Sustainable Development Goals. However, the alcohol industry's role in shaping alcohol policy through international avenues, such as trade fora, is poorly understood. We investigate whether the World Trade Organization (WTO) is a forum for alcohol industry influence over alcohol policy. METHODS: In this qualitative analysis, we studied discussions on alcohol health warning labelling policies that occurred at the WTO's Technical Barriers to Trade (TBT) Committee meetings. Using the WTO Documents Online archive, we searched the written minutes of all TBT Committee meetings available from Jan 1, 1995, to Dec 31, 2019, to identify minutes and referenced documents pertaining to discussions on health warning labelling policies. We specifically sought WTO member statements on health warning labelling policies. We identified instances in which WTO member representatives indicated that their statements represented industry. We further developed and applied a taxonomy of industry rhetoric to identify whether WTO member statements advanced arguments made by industry in domestic forums. FINDINGS: Among 83 documents, comprising TBT Committee minutes, notifications to the WTO of the policy proposal, and written comments by WTO members, WTO members made 212 statements (between March 24, 2010, and Nov 15, 2019) on ten alcohol labelling policies proposed by Thailand, Kenya, the Dominican Republic, Israel, Turkey, Mexico, India, South Africa, Ireland, and South Korea. WTO members stated that their claims represented industry in seven (3·3%) of 212 statements, and 117 (55·2%) statements featured industry arguments. Member statements featured many arguments used by industry in domestic policy forums to stall alcohol policy. Arguments focused on descaling and reframing the nature and causes of alcohol-related problems, promoting alternative policies such as information campaigns, promoting industry partnerships, questioning the evidence, and emphasising manufacturing and wider economic costs and harms. INTERPRETATION: WTO discussions at TBT Committee meetings on alcohol health warnings advanced arguments used by the alcohol industry in domestic settings to prevent potentially effective alcohol policies. WTO members appeared to be influenced by alcohol industry interests, although only a minority of challenges explicitly referenced industry demands. Increased transparency about vested interests might be needed to overcome industry influence. FUNDING: None.

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)0.020
metaresearch head score (Gemma)0.040
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.108

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.040
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.005
Science and technology studies0.0090.009
Scholarly communication0.0060.007
Open science0.0010.007
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.082
GPT teacher head0.433
Teacher spread0.351 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations29
Published2022
Admission routes1
Has abstractyes

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