The European Union investment policy in Asia in the light of „Dawn of an Asian century in international investment law”
Bibliographic record
Abstract
After its gradual establishment, the investment policy of the European Union experienced turbulent times when the EU and the United States commenced negotiations on the Trans-Pacific Trade and Investment Partnership. While the public and political focus concentrated on the transatlantic relations with the United States (TTIP) and Canada (CETA), the EU has steadily progressed at different paces with third countries in Asia where it commenced trade and investment negotiations with Singapore, Vietnam, Myanmar, China, Thailand, the Philippines and Indonesia among others. This paper seeks to evaluate how the Union has been successful in its “Asia strategy” in the field of investment negotiation and promotion of its reform approach to the investment protection regime. It offers an overview of the EU investment negotiations with the individual partners in the Far East and explores these relationships and their potential implications. It concludes that it is not surprising that the EU already persuaded the first countries in this region about its novel approach because of their strong motivation to conclude agreements with the EU that will ‘modernise’ and ‘harmonise’ the existing investment protection. On the other hand, challenges persist as it remains to be seen in which direction Asian actors will push for in the development of global investment governance.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".