Bibliographic record
Abstract
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".