The Use and Misuse of the Corruption Defence in International Investment Arbitration
Bibliographic record
Abstract
This article argues that while a mutually beneficial relationship can be cultivated between international investment arbitration and anti-corruption policies, the recent emergence of a state-invoked 'corruption defence' as a complete defence to liability for alleged breach of investment protection obligations may hamper the sustainability and effectiveness of such a relationship. In the context of a corrupt host state, for instance, and particularly a corrupt developing host state, the growing use of this defence may arguably frustrate the objectives of both foreign investment protection and anti-corruption policies. This was the case, for instance, in the 2006 investment arbitration World Duty Free Co. Ltd. v. Republic of Kenya, in which the arbitral tribunal accepted the corruption defence invoked by Kenya as a complete defence to the investor's claims of alleged breach of investment protection obligations. In so doing, the tribunal arguably disregarded the potentially detrimental effects such a decision may have on Kenya's ability to fight corruption and attract further foreign investment, both of which are of crucial importance to its future development. This article argues, therefore, that investment arbitration tribunals ought to proceed with caution when permitting a corrupt host state, and particularly a developing one, to rely on the corruption defence, and ought to devise alternative remedies to the complete rejection of the claims where investor corruption is established.
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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 | 0.034 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.043 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.001 | 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".