The European Commission and the “Europeanisation” of EU trade diplomacy: the case of EU-China relations, 1999–2021
Bibliographic record
Abstract
Abstract In recent times, the European Commission has shown to have increasing difficulty keeping up its central position in trade diplomacy towards China, in the face of a growing wariness and assertiveness of the member states. In this article, we observe a changing balance, a development from EU diplomacy towards European diplomacy, meaning a growing influence of EU member states in the process. We call this the “Europeanisation” of EU economic diplomacy. This is not limited to China and reflects a broader development, as is visible in the politicisation of the negotiation processes towards the (failed) Transatlantic Trade and Investment Partnership (TTIP) and the (provisionally applied) Comprehensive Economic and Trade Agreement (CETA) between the EU and Canada, and also in the trend towards mixed agreements that require member state ratification. However, it was particularly in the relations with China that such trends became visible for the first time and have continued to manifest themselves throughout the years. Apart from the member states assembled in the Council, also the European Parliament has strengthened its position in the process at the expense of the Commission.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".