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Record W3019104375 · doi:10.7765/9781526133663.00014

European Union trade policy in the wake of the Brexit vote

2020· book-chapter· en· W3019104375 on OpenAlexaboutno aff
Holly Jarman

Bibliographic record

VenueManchester University Press eBooks · 2020
Typebook-chapter
Languageen
FieldSocial Sciences
TopicRegional Development and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsBrexitEuropean unionInternational tradeTransatlantic Trade and Investment PartnershipFree tradeCommercial policyInternational economicsSingle marketLiberalizationTrade barrierEconomic integrationPoliticsMember stateGeneral partnershipOrder (exchange)International free trade agreementNegotiationPolitical scienceEconomicsMember statesMarket economy

Abstract

fetched live from OpenAlex

The European Union's Common Commercial Policy (CCP) has shaped Europe and its relationships with the rest of the world for six decades by giving central EU institutions the ability to negotiate trade deals, defend against foreign trade practices, and set the trade policy agenda for the Union. The United Kingdom often plays a central role in EU trade politics: it has the second largest GDP of any member state, is the bloc's largest exporter of services, and has long been a mainstay of the EU's trade liberalization agenda. This chapter examines the role of the UK in setting current EU trade policy in order to draw conclusions about the likely effects of Brexit, using data from three case studies of recent trade agreements – the EU-Canada Comprehensive Economic and Trade Agreement, the Transatlantic Trade and Investment Partnership, and the EU-Singapore Free Trade Agreement – to analyze critical trade policy decisions and the coalitions supporting them. It concludes that the departure of the UK from the Common Market will have a strong destabilizing effect, not just on the economic and social wellbeing of European countries, but also on the often tenuous political consensus that underlies the CCP.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.990
Threshold uncertainty score0.523

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.050
GPT teacher head0.237
Teacher spread0.187 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2020
Admission routes1
Has abstractyes

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