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Record W2606346615 · doi:10.1515/jafio-2016-0015

Labeling Demands, Coexistence and the Challenges for Trade

2017· article· en· W2606346615 on OpenAlexaff
Stuart J. Smyth, William A. Kerr, Peter W.B. Phillips

Bibliographic record

VenueJournal of Agricultural & Food Industrial Organization · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIntellectual Property and Patents
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsInternational tradeInternational economicsEconomicsCommercial policyTrade barrierLimitingGeneral partnershipTransatlantic Trade and Investment PartnershipTrade agreementBusinessFree trade

Abstract

fetched live from OpenAlex

Abstract As with other facets of regulatory policy for genetically modified organisms, labeling is a contentious issue in international trade policy. Labeling can be a trade barrier. The existing multilateral system for labeling is based in the Sanitary and Phytosanitary (SPS) and Technical Barriers to Trade (TBT) agreements of the World Trade Organization (WTO) – and is focused on limiting the rent-seeking trade inhibiting aspects of labeling. An alternative view of labeling is based on the social policy of consumers’ right to know and takes no account of the trade costs associated with labeling. The labeling rules of the SPS and TBT are explained. These are contrasted with the trade effects of a labeling system based on consumers’ right to know that might be incorporated into a preferential trade agreement. The relative economic effects are explained and contrasted with those of the social policy of coexistence. The difficulties arising from some trading partners using the labeling rules of a preferential trade agreement while others use those of the WTO are outlined. The likelihood of alternative rules for labeling being included in the Transatlantic Trade and Investment Partnership (T-TIP), the Trans-Pacific Partnership (TPP) and the Comprehensive Economic and Trade Agreement (CETA) are examined.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.576
Threshold uncertainty score0.541

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
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 head0.204
GPT teacher head0.234
Teacher spread0.030 · 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

Citations6
Published2017
Admission routes1
Has abstractyes

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