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Record W2884916831 · doi:10.1093/ejil/chy024

Leaders in the Expansive and Restrictive Interpretation of Investment Treaties: A Descriptive Study of ISDS Awards to 2010

2018· article· en· W2884916831 on OpenAlexaff
Gus Van Harten

Bibliographic record

VenueEuropean Journal of International Law · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Arbitration and Investment Law
Canadian institutionsYork University
Fundersnot available
KeywordsExpansiveExpropriationTreatyDiscretionLaw and economicsScope (computer science)Investment (military)Interpretation (philosophy)Settlement (finance)LawInternational investmentEconomicsInvestor-state dispute settlementForeign direct investmentBusinessPolitical scienceFinancePolitics

Abstract

fetched live from OpenAlex

This article provides an empirical analysis of interpretive discretion in investor–state dis- pute settlement (ISDS). Since the late 1990s, foreign investors have brought hundreds of investment treaty claims against states, leading to numerous awards in which arbitrators have interpreted investment treaties. Arbitrators may resolve ambiguities in the treaties in expansive or restrictive ways, thereby affecting the compensatory promise of ISDS for foreign investors and corresponding risks for states. Which arbitrators have contributed most to expansive or restrictive approaches? To examine this question, data was analysed on arbitrators’ resolutions of contested legal issues, such as the permissibility of parallel or minority shareholder claims and the scope of concepts of investment, fair and equita- ble treatment, full protection and security and indirect expropriation. The analysis allows for rankings of arbitrators and tentative descriptive findings identifying a small group of individuals as the leading contributors to expansive resolutions and one individual as the leading contributor to restrictive resolutions. The analysis reveals how interpretive discre- tion impacted relevant legal aspects of ISDS in its first two decades and supplements other research on ISDS arbitrator behaviour.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.863
Threshold uncertainty score0.301

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.032
GPT teacher head0.260
Teacher spread0.228 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations25
Published2018
Admission routes1
Has abstractyes

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