Between Scylla and Charybdis: navigating EU strategic autonomy amid US-China trade competition
Bibliographic record
Abstract
Amid growing Sino-American competition, we would expect the US and China to deploy ‘binding’ and ‘wedging’ strategies to encourage Europe to align with it against the other. As this article shows, however, in the realm of trade, their behaviour has been less strategic – and more haphazard, volatile and contradictory – than theories of great power competition would predict. Driven primarily by domestic political considerations, the US and China’s actions on trade have alienated, antagonized and repelled Europe rather than encouraging it to align with either of them. Instead, external threats from the US and China have served to strengthen EU unity and resolve to maintain its strategic autonomy. Navigating threats from both sides, the EU has charted its own course, seeking to defend the rules-based multilateral trading system, while also developing new tools to better defend its interests, including ones specifically designed to promote internal binding and counter external wedging.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".