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Record W2317217012 · doi:10.1215/03616878-2682621

Evolving Norms at the Intersection of Health and Trade

2014· article· en· W2317217012 on OpenAlexaff
Jeffrey Drope, Raphael Lencucha

Bibliographic record

VenueJournal of Health Politics Policy and Law · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Public Health Policies and Epidemiology
Canadian institutionsMcGill University
FundersNational Institute on Drug Abuse
KeywordsIntersection (aeronautics)Political scienceGeographyCartography

Abstract

fetched live from OpenAlex

There has been growing tension at the intersection of health and economic policy making as global governance has increased across sectors. This tension has been particularly evident between tobacco control and trade policy, as the international norms that frame them -- particularly the Framework Convention on Tobacco Control and the World Trade Organization (WTO) -- have continued to institutionalize. Using five case studies of major tobacco-related trade disputes from the principal multilateral system of trade governance -- the WTO/General Agreement on Tariffs and Trade -- we trace the evolution of these interacting norms over nearly twenty-five years. Our analytic framework focuses on the actors that advance, defend, and challenge these norms. We find that an increasingly broad network, which includes governments, intergovernmental organizations, nongovernmental organizations, and members of the epistemic community, is playing a more active role in seeking to resolve these tensions. Moreover, key economic actors are beginning to incorporate health more actively into their messaging and activities. We also demonstrate that the most recent resonant messages reflect a more nuanced integration of the two norms. The tobacco control example has direct relevance to related policy areas, including environment, safety, access to medicines, diet, and alcohol.

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)0.004
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.894
Threshold uncertainty score0.949

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.036
GPT teacher head0.334
Teacher spread0.299 · 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

Citations52
Published2014
Admission routes1
Has abstractyes

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