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Record W2614737040

Recognition and Responsibility: A Legislative Role for Transnational Corporations in Public International Law? - Thoughts from the Perspective of Human Rights

2015· article· en· W2614737040 on OpenAlexaff
Stefan Kirchner

Bibliographic record

VenueLaCRIS (University of Lapland) · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Law and Human Rights
Canadian institutionsInstitute on Governance
Fundersnot available
KeywordsHuman rightsPerspective (graphical)Political scienceDe factoGovernment (linguistics)LegislatureLaw and economicsInternational human rights lawInternational lawLawPublic administrationPolitical economySociology
DOInot available

Abstract

fetched live from OpenAlex

Transnational Corporations (TNCs) provide goods, services, jobs and tax income for states and are an essential factor of the globalized economy.At the same time are many TNCs so powerful that it has become impossible for some nation states to regulate them adequately.In particular, in cases of human rights violations, TNCs can be underregulated perpetrators.For a long time, there have been efforts to ensure that TNCs can be held accountable even if their economic power exceeds the political and regulatory powers of nation-states.So far, international law has had limited success in this regard.Public International Law might be more effective in reaching TNCs if TNCs would have a more open role (as opposed to lobbying states) in creating new rules of international law.It is suggested in this article that TNCs can have a role in the legislative process.The shortcomings of the current legal system will be shown as well.In addition, the text will provide considerations based on human rights and international constitutional law as to why this should not yet happen at this time.

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)0.020
metaresearch head score (Gemma)0.018
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: Empirical · Consensus signal: none
Teacher disagreement score0.026
Threshold uncertainty score0.107

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.018
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0100.054
Scholarly communication0.0260.028
Open science0.0020.007
Research integrity0.0190.017
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 head0.069
GPT teacher head0.239
Teacher spread0.170 · 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
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
Published2015
Admission routes1
Has abstractyes

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