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Record W1600492370 · doi:10.22329/wyaj.v31i1.4320

TRANSNATIONAL JUDICIAL AND NON-JUDICIAL REMEDIES FOR CORPORATE HUMAN RIGHTS HARMS: CHALLENGES OF AND FOR LAW

2013· article· en· W1600492370 on OpenAlexaffvenue
Sara L. Seck

Bibliographic record

VenueWindsor Yearbook of Access to Justice · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Law and Human Rights
Canadian institutionsWestern University
Fundersnot available
KeywordsHuman rightsCitizenshipInternational human rights lawPolitical scienceLawContext (archaeology)AccountabilityCorporate governanceEconomic JusticeCorporate social responsibilitySociologyLaw and economicsBusinessPolitics

Abstract

fetched live from OpenAlex

This paper will consider whether the polycentric governance approach of the 2011 United Nations Guiding Principles on Business and Human Rights has the potential to achieve the goal of transnational corporate compliance with human rights responsibilities including, importantly, the goal of access to remedy and justice for those who have been harmed. The paper was initially written as a contribution to a conference at the University of Windsor entitled Justice Beyond the State: Transnationalism and Law. First, the paper examines understandings of “citizenship” and “non-citizenship” in relation to transnational corporate [TNC] accountability in the human rights context. Two distinct perspectives are explored: first, TNC citizenship and non-citizenship and the rights and responsibilities that flow from these; and second, citizenship and non-citizenship of victims of human rights violations in relation to rights of access to remedy. Together, these insights inform an understanding of the role that transnational law and legal pluralism beyond the state could serve in facilitating remedy for human rights violations. Specifically, the paper will conclude with reflections on what might be required for implementation of the UN Guiding Principles to achieve the goal of transnational corporate compliance and access to remedy for victims of rights violations.

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 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)0.000
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score0.595

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.087
GPT teacher head0.289
Teacher spread0.203 · 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 teacher head, 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

Citations6
Published2013
Admission routes2
Has abstractyes

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