Managing Disintegration: How the European Parliament Responded and Adapted to Brexit
Bibliographic record
Abstract
Brexit makes both a direct and an indirect impact on the European Parliament (EP). The most direct consequence is the withdrawal of the 73-member strong UK contingent and the changing size of the political groups. Yet, the impact of Brexit is also felt in more oblique ways. Focussing on the role and influence of the EP in the EU–UK negotiations, and of the British delegation in the EP, this article shows that the process, and not just the outcome of Brexit, has significant organisational implications for the EP and its political groups. Moreover, it also showcases the importance of informal rules and norms of behaviour, which were affected by Brexit well ahead of any formal change to the UK status as a Member State. The EP and its leadership ensured the active involvement of the EP in the negotiating process—albeit in different ways for the withdrawal agreement and the future relationship—and sought to minimise the costs of Brexit, reducing the clout of British members particularly in the allocation of legislative reports.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.054 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.033 |
| Scholarly communication | 0.029 | 0.015 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.014 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".