The Problem of Execution Immunities and the ICSID Convention
Bibliographic record
Abstract
Abstract The prevailing view in international practice is that, by consenting to arbitration, a State does not waive its immunity from execution. Yet, in the context of arbitration administered by the International Centre for Settlement of Investment Disputes (ICSID) – as in the context of arbitration between States and private parties more generally – the problem of execution immunities is a very significant obstacle to the effective implementation of arbitral awards. When immunity from execution allows States to escape obligations they have freely undertaken, and when it withholds from claimants the fruits of a favourable award, the benefits of arbitration become illusory. This article contends that the prevailing view is no longer compelling. We argue that domestic courts can and should uphold the rule-of-law objectives and benefits of the ICSID Convention by adjusting their approach to immunity claims in the arbitral context: consent to arbitration should be interpreted as an implied waiver of immunity from execution.
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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 | 0.043 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.034 |
| Scholarly communication | 0.018 | 0.013 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.020 | 0.029 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".