MétaCan
Menu
Back to cohort
Record W5022358 · doi:10.22004/ag.econ.135067

The EU-Canada Free Trade Agreement: What is on the Table for Agriculture?

2012· preprint· en· W5022358 on OpenAlexaffabout
William A. Kerr

Bibliographic record

VenueAgEcon Search (University of Minnesota, USA) · 2012
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Economics and Policy
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsSubsidyNegotiationInternational tradeAgricultureHarmonizationFree tradeTrade barrierTariffMultilateral trade negotiationsInternational economicsFree trade agreementBusinessPhytosanitary certificationTechnical barriers to tradeEconomic integrationEconomicsPolitical scienceEconomic growthGeographyLaw

Abstract

fetched live from OpenAlex

In October 2008 French President Nicholas Sarkozy and Canadian Prime Minister Stephen Harper announced that the EU and Canada would seek a free trade agreement and in May 2009 negotiations on a Comprehensive Economic and Trade Agreement (CETA) commenced. There have been a number of negotiating sessions since then and good progress has been reported. One of the more difficult sectors was expected to be agriculture. This paper outlines the major opportunities for expanded agricultural trade between the EU and Canada as well as those areas where the negotiations are expected to be particularly difficult. Topics include, subsidies, sanitary and phytosanitary barriers to trade, tariffs, tariff line adjustments, regulatory harmonization, protection for geographical indications, barriers to trade in genetically modified products and TRQs in the Canadian dairy sector. A section on opportunities and concerns of particular interest to the agri-food sector of the UK is included. The paper concludes with a discussion of the expected outcome and degree of trade expansion that will follow a successful conclusion to the negotiations.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.379
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.038
GPT teacher head0.204
Teacher spread0.165 · 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

Citations1
Published2012
Admission routes2
Has abstractyes

Explore more

Same venueAgEcon Search (University of Minnesota, USA)Same topicAgricultural Economics and PolicyFrench-language works237,207