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Record W6986213093

Overcoming ‘Frankenfoods’ and ‘secret courts’: the\nresilience of EU trade policy. College of Europe Policy Brief #9.18

2018· other· en· W6986213093 on OpenAlexaboutno aff

Bibliographic record

VenueArchive of European Integration (AEI) (University of Pittsburgh) · 2018
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsScrutinyMember stateEuropean unionTrade barrierFree tradeNegotiationCommercial policyOpposition (politics)Investment (military)Deadlock
DOInot available

Abstract

fetched live from OpenAlex

Despite vocal contestation and fears of domestic\ninstitutional deadlock over its trade negotiations,\nthe European Union has proven resilient in its trade\npolicy, notably by concluding bilateral trade and\ninvestment agreements with important partners,\nincluding two across the North Atlantic, Canada\nand Mexico.\n> A professionally orchestrated NGO campaign\nagainst TTIP and CETA that fed scepticism in several\nEU member states was crowned with mixed\nsuccess. Whereas TTIP negotiations were put on\nhold, CETA finally proceeded. The lack of a broad\npan-European opposition and the strong\nconsensual decision-making processes in the EU\nincentivised policy-makers to accommodate\nobjections, tread carefully and craft compromise.\n> This process has been further facilitated by the May\n2017 Singapore ruling of the Court of Justice of the\nEU which created room for trade agreements to be\nsplit according to exclusive and shared\ncompetences. As a result, new agreements such as\nthose with Singapore or Japan now typically\nembrace three agreements in order to expedite\nratification: trade, investment protection and\npolitical cooperation.\n> Separating trade and investment agreements\nmakes it more difficult for special interests to hold\nfree trade agreements hostage and locates\nparliamentary scrutiny at the European level, while\ninvestment agreements face additional ratification\nby member state parliaments – and a pending\nCourt Opinion.

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 categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.880
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.004
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 head0.015
GPT teacher head0.233
Teacher spread0.218 · 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
GenreOther

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
Published2018
Admission routes1
Has abstractyes

Explore more

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