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Record W2598301682

Short-Interval Cortical Inhibition and Rhetoric and the Law, Or the Law of Rhetoric: How Countries Oppose Novel Tobacco Control Measures at The World Trade Organization

2016· article· en· W2598301682 on OpenAlexaboutno aff
Raphael Lencucha, Jeffrey Drope, Ronald Labonté

Bibliographic record

Venuee-Publications@Marquette (Marquette University) · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Public Health Policies and Epidemiology
Canadian institutionsnot available
FundersNational Cancer InstituteFogarty International CenterNational Institutes of Health
KeywordsRhetoricLawPolitical scienceControl (management)EconomicsPhilosophyTheology
DOInot available

Abstract

fetched live from OpenAlex

The tobacco industry has developed an extensive array of strategies and arguments to prevent or weaken government regulation. These strategies and arguments are well documented at the domestic level. However, there remains a need to examine how these arguments are reflected in the challenges waged by governments within the World Trade Organization (WTO). Decisions made at the WTO have the potential to shape how countries govern. Our analysis was conducted on two novel tobacco control measures: tobacco additives bans (Canada, United States and Brazil) and plain, standardized packaging of tobacco products (Australia, New Zealand, Ireland, EU and UK). We analyzed WTO documents (i.e. meeting minutes and submissions) (n = 62) in order to identify patterns of argumentation and compare these patterns with well-documented industry arguments. The pattern of these arguments reveal that despite the unique institutional structure of the WTO, country representatives opposing novel tobacco control measures use the same non-technical arguments as those that the tobacco industry continues to use to oppose these measures at the domestic level.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.906
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.001
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 head0.027
GPT teacher head0.233
Teacher spread0.206 · 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.

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

Citations0
Published2016
Admission routes1
Has abstractyes

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