MétaCan
Menu
Back to cohort

Agricultural Biotechnology and Food Security: Can CETA, TPP, and TTIP Become Venues to Facilitate Trade in GM Products?

2017· book-chapter· en· W2737786738 on OpenAlexaffabout
Crina Viju, Stuart J. Smyth, William A. Kerr

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldAgricultural and Biological Sciences
TopicGenetically Modified Organisms Research
Canadian institutionsUniversity of SaskatchewanCarleton University
Fundersnot available
KeywordsFood securityInternational tradeAgricultural biotechnologyAgricultureBusinessBiotechnologyBiology

Abstract

fetched live from OpenAlex

Abstract Strong evidence has shown that increased agricultural productivity and opened international trade are required to maintain and enhance food security. The multilateral trading system has been unable to keep trade open for one subset of agricultural products – those that use biotechnology in production. This chapter assesses whether preferential trade agreements can represent potential alternative sources of trade rules for dealing with trade in the products of biotechnology. This chapter analyzes and compares three case studies of preferential trade agreements (Canada-EU Comprehensive Economic and Trade Agreement, EU-US Transatlantic Trade and Investment Partnership, and Trans-Pacific Partnership), by focusing on negotiations pertaining to products of biotechnology. The three preferential trade agreements have shown little inventiveness in their attempts to put in place rules of trade for the products of modern agricultural biotechnology and have established forums where only issues can be discussed. They are forums to talk and talk without any means to force closure on negotiations. Given the inability to deal with the issue of biotechnology at the WTO or other multilateral forums, the recent and current negotiations of major preferential agreements represent the second best alternative which still needs to be analyzed and still needs to be understood by policy makers, academics, and the population at large. This chapter represents a first step in that direction.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.610
Threshold uncertainty score0.819

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.059
GPT teacher head0.227
Teacher spread0.168 · 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 designOther design
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

Citations1
Published2017
Admission routes2
Has abstractyes

Explore more

Same topicGenetically Modified Organisms ResearchFrench-language works237,207