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Record W2229524308

Time to Say Local Cheese and Smile at Geographical Indications of Origin? International Trade and Local Development in the United States

2015· article· en· W2229524308 on OpenAlexaboutno aff
Irene Calboli

Bibliographic record

VenueeYLS (Yale Law School) · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicOrganic Food and Agriculture
Canadian institutionsnot available
Fundersnot available
KeywordsInternational tradeEuropean unionNegotiationTransatlantic Trade and Investment PartnershipGeneral partnershipPosition (finance)Product (mathematics)Quality (philosophy)BusinessPolitical scienceLawFinance
DOInot available

Abstract

fetched live from OpenAlex

In this Article, I offer some considerations on a possible compromising solution for the controversy between the European Union (EU) and the United States (U.S.) on the regulation of geographical indications of origin (GIs) as part of the negotiations in the Transatlantic Trade and Investment Partnership (TTIP). Notably, I advocate that the EU and the U.S. consider adopting a solution similar to that adopted in the Canada and European Union Comprehensive Economic and Trade Agreement (CETA). In particular, I note that, even though CETA accepted several of the EU's requests to claw-back names that were not previously protected in Canada, it also includes important exceptions to balance the effect of this claw-back process with respect to several (highly contested) names at issue. Thus, the solution adopted in CETA represents a win-win solution for Canada and the EU, and a similar solution could resolve the GI controversy in the TTIP. My position in this Article is that, far from being just an "EU thing," an appropriate level of GI protection can promote local businesses, high(er) quality products, and more accurate consumer information about products everywhere, including in the U.S. Notably, a rigorous system of GI protection-one that is based on products grown and manufactured locally and where geographical names are protected against misuse from parties operating outside the geographical areas-would provide more accurate product information to U.S. consumers and could motivate U.S. producers to invest in and maintain high(er) quality local products. In turn, this could lead to more innovation in the U.S. food and agricultural sectors and higher quality products for U.S. consumers. U.S. negotiators do not need to look outside the U.S. to prove the validity of this argument. Instead, they can simply refer to the protection that the U.S. has historically granted to appellations of origin for U.S.-produced wines. Wines produced in Napa, Sonoma, and over thirty U.S. geographical areas are protected under sui generis protection, are well known as high quality products, and are successfully sold worldwide. Thus, the current opposition of certain special interest groups should not deter U.S. negotiators from pursuing a CETA-type solution to resolve the GI controversy between the U.S. and the EU in the TTIP, as this solution is desirable and would benefit in the long term both U.S. producers and U.S. consumers.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.101
Threshold uncertainty score0.200

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.003
Scholarly communication0.0080.004
Open science0.0010.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0140.001

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 head0.013
GPT teacher head0.216
Teacher spread0.203 · 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 source (direct Gemma or distilled Codex), 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

Citations8
Published2015
Admission routes1
Has abstractyes

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