The EU’S Proposed Investment Court and WTO Dispute Settlement: A Comparison and Lessons Learned
Bibliographic record
Abstract
Investor-state dispute settlement (ISDS) has been a prominent feature of bilateral investment treaties that have proliferated since the mid-20th century. Perhaps in part due to its popularity, ISDS has recently been subject to significant criticism. The European Union has proposed a reconception of ISDS in the Comprehensive Economic and Trade Agreement with Canada (CETA), which was signed on 30 October 2016, in its proposed Investment Chapter for the Transatlantic Trade and Investment Partnership (TTIP) which it was negotiating with the United States until those negotiations were effectively paused in early 2017, as well as in several other policy documents and free trade agreements. The EU’S proposed “Investment Court System” (ICS) in particular, would make resolution of investment disputes more similar to the World Trade Organisation’s (WTO) state-to-state dispute settlement mechanism for trade disputes and, as the EU sees it, would form the basis for a broader restructuring and multilateralisation of investment protection worldwide. This article examines the EU’S proposed approach and aims to compare key aspects of its Investment Court mechanism to the WTO. Where possible, we also consider some of the lessons learned from the WTO and how they compare to the EU’S proposed Investment Court System.
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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 | 0.037 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.023 | 0.019 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.013 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".