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Record W4386489406 · doi:10.60082/2817-5069.3815

Between the Devil and the Deep Blue Sea—Towards Access to Justice for Local Communities in Investor-state Arbitration or Business and Human Rights Arbitration

2022· article· en· W4386489406 on OpenAlexvenueno aff
Akinwumi Ogunranti

Bibliographic record

VenueOsgoode Hall law journal · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Arbitration and Investment Law
Canadian institutionsnot available
Fundersnot available
KeywordsArbitrationCompulsory arbitrationTribunalHuman rightsBusinessLawInternational arbitrationParallelsLaw and economicsEconomicsPolitical science

Abstract

fetched live from OpenAlex

This article focuses on the proposal to adapt international arbitration to business disputes involving human rights. The Business and Human Rights arbitration (BHR arbitration) proposal seeks to give local communities who are victims of multinational corporations’ human rights and environmental abuses access to justice in a specialized international BHR arbitration tribunal. Through a comparison between investor-state arbitration (ISA) and BHR arbitration, this article contends that it would be more efficient to reform ISA than to create a BHR arbitration tribunal. Reforming ISA would avoid the possible parallel arbitration systems that may arise from the duplication of international governance efforts. It would also reduce local communities’ need to resort to transnational litigation, which is procedurally complex and often unsuccessful. Therefore, the possibility of ISA reform makes the BHR arbitration proposal superfluous or, at best, limited in its potential application. Creating a new arbitral structure that is untested and fraught with procedural and substantive complexities may not be worth the trouble. Considering the parallels between the ISA and proposed BHR arbitration, and the prospect of creating a one-stop shop for business and human rights abuse, this article suggests that BHR arbitration is an unnecessary governance effort in international arbitration and a distraction from necessary ISA reform.

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)0.013
metaresearch head score (Gemma)0.011
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 score0.013
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0130.040
Scholarly communication0.0120.012
Open science0.0010.021
Research integrity0.0060.009
Insufficient payload (model declined to judge)0.0050.001

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 head0.045
GPT teacher head0.282
Teacher spread0.236 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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