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Record W2313650210 · doi:10.1350/enlr.2014.16.3.216

Enhancing the Accountability of Transnational Corporations: The Case for ‘Decoupling’ Environmental Issues

2014· article· en· W2313650210 on OpenAlexaboutno aff
Ciprian N. Radavoi, Yongmin Bian

Bibliographic record

VenueEnvironmental Law Review · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Law and Human Rights
Canadian institutionsnot available
Fundersnot available
KeywordsAccountabilityHuman rightsChinaWrongdoingDue diligenceCorporate social responsibilitySanctionsBusinessPolitical scienceLaw and economicsEconomicsLaw

Abstract

fetched live from OpenAlex

The lack of accountability of transnational corporations (TNCs) for any harmful behaviour in the fields of environment, labour and human rights is a concern for the global community. Despite various attempts, neither the United Nations nor the home or host countries of most TNCs have so far provided any effective, binding solutions. This article argues that an important reason for the lack of advancement in introducing greater accountability is because issues such as workers' rights, the environment and human rights are often discussed together. A new approach, one that is solely focused on protecting the environment, is desirable especially with the rise of new capital exporters. In 2013, China detached the issue of the environment from those of workers' or human rights, in its attempt to tackle overseas corporate wrongdoing. Its environmental guidelines are worth emulating, but it lags behind in areas such as human rights. Analysing the position of the environment among the other fields involved in the debate, we first identify several theoretical reasons for detaching the former from an international law perspective. We then provide a comparative functional analysis of four extraterritorial corporate social responsibility Bills – those in the United States (2000), Australia (2000), the United Kingdom (2002) and Canada (2009) – all of which were rejected by their national parliaments. This lends additional support to the thesis that including the environment with other targeted fields stands in the way of home countries improving the environmental behaviour of their overseas corporations.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.930
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.239
Teacher spread0.218 · 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.

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

Citations5
Published2014
Admission routes1
Has abstractyes

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