Counterclaims in Investment Arbitration: Towards an Integrated Approach
Bibliographic record
Abstract
Abstract It is widely accepted that counterclaims may not be used as a means to exceed the limits of a tribunal’s jurisdiction, while they must at the same time be closely connected to the other party’s claim. However, in the particular context of investment arbitration, these two propositions create a tension that has led to a problematic and unfair construction of the investor-State dispute settlement mechanism. This article explores the foundations for a better, more integrated approach. It argues that jurisdiction over counterclaims should be seen as incidental to the one already established over the original claim, allowing tribunals to hear a wider range of counterclaims. This is inherent to the exercise of a tribunal’s adjudicative function, and rests on a foundation not only of implied consent, but also on one of principle. The approach presented here has the potential to protect counterclaims as a matter of procedural fairness and to address some of the reasons for the backlash against international investment arbitration law.
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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 | 0.065 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.006 | 0.048 |
| Scholarly communication | 0.029 | 0.031 |
| Open science | 0.006 | 0.022 |
| Research integrity | 0.021 | 0.024 |
| Insufficient payload (model declined to judge) | 0.004 | 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".