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Record W3037858832 · doi:10.1163/22119000-12340177

The Diversity Deficit in International Investment Arbitration

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

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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: none
Teacher disagreement score0.899
Threshold uncertainty score0.404

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 science0.0010.000
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 head0.225
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