Systemic rivalry and balancing interests: Chinese investment meets EU law on the Belt and Road. CEPS Policy Insights No 2019-04 /21 March 2019
Bibliographic record
Abstract
For years, the EU has refrained from criticising China’s attempts to shape globalisation according to its own interests. Member states have allowed the Belt and Road Initiative (BRI) to tip the balance of power towards the companies that China owns or subsidises. Alarmed by recent Chinese takeovers in strategic industries, the EU has flagged up its intention to toughen rules on foreign investment flows into Europe. The brand-new EU Strategic Outlook on China adopts a multifaceted approach and defines the ‘Middle Kingdom’ simultaneously as a cooperation and negotiation partner with whom the Union needs to find a balance of interests, an “economic competitor” in pursuit of technological leadership, and a “systemic rival” promoting alternative models of governance. This paper takes stock of BRI investments in Europe and of member states’ concerns about economic and national security. It then examines the EU-wide legal bulwarks and regulatory responses that are intended to hedge against unfair practices. It concludes that, while a more realistic and assertive European approach toward Chinese market behaviour is welcome, the EU should take China up on its pledge to embolden the BRI with ‘soft connectivity’, i.e. a legal infrastructure, rather than risk mutual harm by adopting too protectionist a stance. This should benefit not just the EU and China but also the other ‘16+1’ countries along the central corridor of the BRI, which passes through the Caucasus, the Balkans and Eastern Europe – all in the spirit of the EU’s 2018 connectivity strategy with Asia.
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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 | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.008 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".