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Record W2946817401 · doi:10.1080/07036337.2019.1599372

Negotiating Brexit: the European Parliament between participation and influence

2019· article· en· W2946817401 on OpenAlexaff
Edoardo Bressanelli, Nicola Chelotti, Wilhelm Lehmann

Bibliographic record

VenueJournal of European Integration · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicEuropean Union Policy and Governance
Canadian institutionsCentre for International Governance Innovation
FundersEconomic and Social Research Council
KeywordsBrexitParliamentNegotiationDocumentationTreatyPolitical scienceEuropean unionState (computer science)Public administrationPower (physics)Member stateLawPolitical economyPoliticsInternational tradeSociologyBusinessMember statesComputer science

Abstract

fetched live from OpenAlex

Article 50 of the Treaty of Lisbon gives the European Parliament (EP) the power to consent on the terms of the withdrawal agreement between the exiting state and the EU. As Brexit is the first case where art. 50 has been invoked, the role of the EP in such a procedure is uncharted territory. This article assesses to what extent the EP has contributed to the Brexit negotiations until November 2018. Drawing on official documentation and thirteen original interviews with EU policy-makers, it maps the Parliament’s organisational adaptation to prepare itself for the challenge. Through its steering group and coordinator, and by carefully issuing resolutions, the EP has managed to become a ‘quasi-negotiator’. More difficult to detect is the EP’s actual influence, as its preferences were closely aligned to those of the other EU institutions. Overall, the EP had a selective attention in the process, primarily focusing on citizens’ rights.

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)0.044
metaresearch head score (Gemma)0.042
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 score0.044
Threshold uncertainty score0.233

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0440.042
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0110.043
Scholarly communication0.0220.012
Open science0.0020.015
Research integrity0.0090.008
Insufficient payload (model declined to judge)0.0050.001

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 head0.031
GPT teacher head0.314
Teacher spread0.284 · 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

Citations27
Published2019
Admission routes1
Has abstractyes

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