Bibliographic record
Abstract
The decision by the people of the United Kingdom (UK) to vote in a referendum on 23 June 2016 to leave the European Union (EU) has produced shock-waves across Europe and the world. While the Treaty on European Union explicitly allows a Member State to withdraw from the Union, no country thus far had ever decided to secede from what is arguably the most successful experiment in regional integration in history. Brexit, therefore, calls into question consolidated assumptions on the finality of the EU, and simultaneously opens new challenges—not only in the institutional fabric of Europe, but also in the UK constitutional settlement, eg in Northern Ireland and Scotland. This book provides a first comprehensive analysis of the challenges posed by Brexit, their causes, and their consequences. By combining the contributions of lawyers, political scientists, and political economists from across Europe, the book seeks to shed light on the manifold and complicated effects that Brexit creates—in the UK, and its internal constitutional settlement, as well as in the EU, and its institutional regime. While many uncertainties still surround the process of the UK’s withdrawal from the EU, so much is already on the table: this book thus avoids speculation and focuses instead on the many and difficult political, legal, and economic issues that Brexit exposed.
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 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.028 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".