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Record W4319595846 · doi:10.21814/perspectivas.4565

The Legal Nature of New Generation Free Trade Agreements: Lessons from the CETA Saga

2022· article· en· W4319595846 on OpenAlexaboutno aff
Francisco Pereira Coutinho

Bibliographic record

VenuePerspectivas - Journal of Political Science · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Arbitration and Investment Law
Canadian institutionsnot available
Fundersnot available
KeywordsEuropean unionInternational tradeMember statesSWORDFree trade agreementDamagesPolitical scienceFree tradeInternational free trade agreementBusinessLawEngineering

Abstract

fetched live from OpenAlex

The Comprehensive Economic and Trade Agreement (CETA) is a “new generation” bilateral “mixed” free trade agreement signed on 30 October 2016 between Canada and the European Union alongside its Member States. In the European Union, “mixed” trade agreements follow an adoption procedure that determines, in the best-case scenario, a substantial delay to their entry into force, and, in the worst, a veto by Member States that damages the international standing of the European Union. The European Com­mission bowed to Member States’ pressure and decided to qualify CETA as a “mixed agreement” instead of an “EU-only agreement”. That the “guardian of the Treaties” had no constitutional leeway on the choice of CETA’s approval form became clear after the decision of the Court of Justice on the free trade agreement nego­tiated between the European Union and Singapore (Opinion 2/15). CETA’s “mixed” nature determines that its application is limited and provisional, and under a permanent Damocles sword existential threat.

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)0.023
metaresearch head score (Gemma)0.037
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 score0.026
Threshold uncertainty score0.121

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.037
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0100.051
Scholarly communication0.0260.036
Open science0.0030.008
Research integrity0.0180.022
Insufficient payload (model declined to judge)0.0080.001

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 head0.033
GPT teacher head0.288
Teacher spread0.255 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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