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Record W2975141772 · doi:10.21083/surg.v6i2.2573

Global laws for a global economy: A case for bringing multinational corporations under international human rights law

2013· article· en· W2975141772 on OpenAlexaffvenue
Alicia Grant

Bibliographic record

VenueSURG Journal · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Law and Human Rights
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsMultinational corporationHuman rightsEnforcementCorporate governanceState (computer science)International lawGlobalizationArgument (complex analysis)Law and economicsBusinessLawSoft lawInternational human rights lawPolitical scienceEconomicsFinance

Abstract

fetched live from OpenAlex

Economic globalization has created a governance gap, often leaving powerful corporations largely unregulated. The result has been frequent and gross violations of human rights that too often go unpunished. This article outlines the mechanisms that currently exist for regulating the activities of multinational corporations including: (i) corporate self-regulation; (ii) regulation within the state where a company is operating (the host state); (iii) regulation within the state where a parent company is incorporated (the home state); and (iv) codes of conduct at the international level. The advantages and insufficiencies of each level are highlighted, and an argument is subsequently made that the governance gap will only be filled if firms are subjected to binding international law. The article then turns to an examination of international human rights law and discusses the place of non-state actors within this framework. It finds that corporations do have obligations under international human rights law despite the fact that systems for enforcing these duties do not currently exist. The final section discusses the difficulties that might be associated with creating enforcement mechanisms. The article ultimately argues that binding regulation at the international level is necessary in the long run; however, due to the difficulties in achieving this objective, regulation should also continue to be improved at the company, industry, host-state, and home-state levels. Keywords: multinational corporations; international law; human rights; corporate activity (regulation of)

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)0.014
metaresearch head score (Gemma)0.012
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 score0.017
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0080.048
Scholarly communication0.0170.013
Open science0.0010.010
Research integrity0.0130.014
Insufficient payload (model declined to judge)0.0040.001

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 head0.031
GPT teacher head0.271
Teacher spread0.240 · 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

Citations1
Published2013
Admission routes2
Has abstractyes

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