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Record W1500727388 · doi:10.54648/joia2013018

The Use and Misuse of the Corruption Defence in International Investment Arbitration

2013· article· en· W1500727388 on OpenAlexaff
Tamar Meshel

Bibliographic record

VenueJournal of International Arbitration · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Arbitration and Investment Law
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsArbitrationLanguage changeInvestment arbitrationBusinessInternational investmentInvestment (military)LawForeign direct investmentInternational tradeLaw and economicsPolitical scienceEconomicsPolitics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.034
metaresearch head score (Gemma)0.044
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.034
Threshold uncertainty score0.180

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0340.044
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0070.043
Scholarly communication0.0160.010
Open science0.0020.007
Research integrity0.0100.012
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.028
GPT teacher head0.244
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2013
Admission routes1
Has abstractyes

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