Assessing What Brexit Means for Europe: Implications for EU Institutions and Actors
Bibliographic record
Abstract
With the signing of the EU–UK trade and cooperation agreement in December 2020, the configurations of Brexit have started to become clearer. The first consequences of the UK’s decision to leave the EU have become visible, both in the UK and in the EU. This thematic issue focuses on a relatively under-researched aspect of Brexit—what the UK withdrawal has meant and means for the EU. Using new empirical data and covering most (if not all) of the post-2016 referendum period, it provides a first overall assessment of the impact of Brexit on the main EU institutions, institutional rules and actors. The articles in the issue reveal that EU institutions and actors changed patterns of behaviour and norms well before the formal exit of the UK in January 2020. They have adopted ‘counter-measures’ to cope with the challenges of the UK withdrawal—be it new organizational practices in the Parliament, different network dynamics in the Council of the EU or the strengthening of the Franco-German partnership. In this sense, the Union has—so far—shown significant resilience in the wake of Brexit.
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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 | 0.019 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".