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Record W2913373523

Data Exclusivities and the Limits to TRIPS Harmonization

2018· article· en· W2913373523 on OpenAlexaboutno aff
Peter K. Yu

Bibliographic record

VenueeYLS (Yale Law School) · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIntellectual Property and Patents
Canadian institutionsnot available
Fundersnot available
KeywordsTRIPS architectureIntellectual propertyHarmonizationTRIPS AgreementNegotiationPolitical scienceInternational tradeEnforcementPoliticsBusinessLawEngineering
DOInot available

Abstract

fetched live from OpenAlex

2019 marks the silver anniversary of the WTO TRIPS Agreement. Policymakers and commentators remain deeply divided about the strengths and limitations of this agreement. On the one hand, they marvel at its success in establishing international minimum standards for the protection and enforcement of intellectual property rights. On the other hand, they widely criticize the agreement for imposing high "one size fits all" standards upon developing countries.Regardless of one's perspective, the harmonization project advanced by the TRIPS Agreement, and continued through TRIPS-plus bilateral, regional and plurilateral agreements, has been at the forefront of the international intellectual property debate. While this article is interested in exploring this continuously controversial project, the discussion will focus on a topic that international intellectual property scholars have underexplored: the limits to TRIPS harmonization.To help examine these limits, this article focuses on the protection of undisclosed test or other data for pharmaceutical and agrochemical products. It begins by discussing issues on which the TRIPS negotiating parties had achieved consensus or had failed to do so. The article then examines the negotiation of new international minimum standards under the TPP Agreement, the proposed RCEP Agreement and the recently completed United States–Mexico–Canada Agreement (USMCA).The article continues to identify three sets of additional complications that have affected the efforts to develop international minimum standards at both the multilateral and nonmultilateral levels. Specifically, the article examines the arrival of new technologies, new politics and new regimes. It concludes by drawing six distinct lessons regarding the TRIPS harmonization project.

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)0.001
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 score0.408
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.003

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 head0.120
GPT teacher head0.258
Teacher spread0.138 · 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

Citations3
Published2018
Admission routes1
Has abstractyes

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