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Record W3037858832 · doi:10.1163/22119000-12340177

The Diversity Deficit in International Investment Arbitration

2020· article· en· W3037858832 on OpenAlexaff
Andrea K. Bjorklund, Daniel Behn, Susan D. Franck, Ćhiara Giorgetti, Won Kidane, Arnaud de Nanteuil, Emilia Onyema

Bibliographic record

VenueThe Journal of World Investment & Trade · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Arbitration and Investment Law
Canadian institutionsMcGill University
Fundersnot available
KeywordsLegitimacyDiversity (politics)ArbitrationInvestor-state dispute settlementCommissionPolitical scienceInternational lawInternational arbitrationInternational investmentBusinessLawLaw and economicsEconomicsForeign direct investment

Abstract

fetched live from OpenAlex

Abstract The United Nations Commission on International Trade Law (UNCITRAL) Working Group III on ISDS (Investor-State Dispute Settlement) Reform considers issues of adjudicator diversity to be an area of concern for the legitimacy of the ISDS system. Studies show that nearly all of the most prominent and repeatedly appointed arbitrators in ISDS cases are men from the Global North with significant prior experience in ISDS cases. Rather than being seen as fair, just, and devoid of bias, decisions are sometimes suspected to be the products of adjudicators who share a particular world view. This article focuses on four key issues: (1) how a lack diversity affects the real and perceived legitimacy of the ISDS system; (2) empirical evidence on the current extent of the diversity problem in ISDS; (3) the causes of the perpetuation of the diversity deficit in ISDS; and (4) what can be done to improve diversity in ISDS.

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)0.023
metaresearch head score (Gemma)0.090
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.124

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.090
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0050.005
Science and technology studies0.0040.015
Scholarly communication0.0130.013
Open science0.0020.012
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0050.000

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 head0.225
Teacher spread0.193 · 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 designQualitative
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

Citations58
Published2020
Admission routes1
Has abstractyes

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