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Record W2614367167 · doi:10.1515/jafio-2016-0016

Approaches to Set Rules for Trade in the Products of Agricultural Biotechnology. Is Harmonization under Trans-Pacific Partnership Possible?

2017· article· en· W2614367167 on OpenAlexaff
Crina Viju, William A. Kerr, Stuart J. Smyth

Bibliographic record

VenueJournal of Agricultural & Food Industrial Organization · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicWorld Trade Organization Law
Canadian institutionsUniversity of SaskatchewanCarleton University
Fundersnot available
KeywordsInternational tradeHarmonizationNegotiationGeneral partnershipAgricultureFree tradeAgricultural biotechnologyLiberalizationEconomicsInternational economicsIntellectual propertyBusinessPolitical scienceBiologyLaw

Abstract

fetched live from OpenAlex

Abstract Given the absence of progress toward a multilateral agreement on trade liberalization in the WTO’s Doha Round, countries are attempting to gain the perceived gains from trade through the negotiation of preferential trade agreements. One of the most ambitious attempts to negotiate a preferential agreement is the Trans Pacific Partnership (TPP) which encompasses 12 countries across the Pacific including both the US and Japan. The TPP members account for approximately 40 % of global GDP. One of the most difficult issues in current international trade policy is the regulation of trade in the products of modern agricultural biotechnology. This question was on the negotiating agenda of the TPP. The objective of this paper is to lay out the major issues in the trade of products of modern agricultural biotechnology and examines the regulatory regimes for biotechnology in the 12 TPP countries. It finds that there is a significant divergence in the approaches to regulating genetically modified organisms (GMOs) across the TPP countries. As a result, the development of a harmonized regulatory regime to govern trade in GMOs was impossible directly in the TPP. A forum where the development of a harmonized system could potentially be undertaken was, however, agreed in the TPP.

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)0.002
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 score0.620
Threshold uncertainty score0.640

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.191
GPT teacher head0.289
Teacher spread0.098 · 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

Citations2
Published2017
Admission routes1
Has abstractyes

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