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Record W4309818200 · doi:10.1017/9781009076425

Corporate Environmental Responsibility in Investor-State Dispute Settlement

2022· book· en· W4309818200 on OpenAlexfundno aff
Tomoko Ishikawa

Bibliographic record

VenueCambridge University Press eBooks · 2022
Typebook
Languageen
FieldBusiness, Management and Accounting
TopicInternational Arbitration and Investment Law
Canadian institutionsnot available
FundersGovernment of CanadaVattenfall
KeywordsDue diligenceInvestor-state dispute settlementSettlement (finance)BusinessCorporate social responsibilityState responsibilityLaw and economicsState (computer science)DiligenceAccountingPolitical scienceInternational lawLawForeign direct investmentInternational investmentEconomicsFinance

Abstract

fetched live from OpenAlex

This book explores the potential of the current investor-state dispute settlement (ISDS) mechanism to materialise the responsibility of foreign investors through the states' counterclaims and defences at the jurisdictional, merits, and quantum phases. In doing so, it seeks to incorporate the recent developments of ISDS in both international and domestic laws of certain jurisdictions on corporate responsibility, including the parent company's due diligence and legal effects of corporations' voluntary commitments. The book also reflects the interests and perspectives of the victims who suffered loss and injury due to investors' conduct. The author demonstrates that the current system does have the inherent potential to advance responsible investment, even though reforms are needed to overcome its limitations. Fully utilising this potential to reflect investor responsibility in IIA-based dispute settlement mechanisms will help to develop practices based on greater due diligence and responsible business conduct.

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)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.006
Scholarly communication0.0060.004
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0070.002

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 head0.024
GPT teacher head0.185
Teacher spread0.161 · 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 designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations8
Published2022
Admission routes1
Has abstractyes

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