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Record W3126028662

Disclosing Disclosure's Defects: Addressing Corporate Irresponsibility for Human Rights Impacts

2015· article· en· W3126028662 on OpenAlexaboutno aff
Marcia Narine

Bibliographic record

VenueUniversity of Miami School of Law Institutional Repository (University of Miami) · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Law and Human Rights
Canadian institutionsnot available
Fundersnot available
KeywordsHuman rightsBusinessCorporate governanceInternational human rights lawLaw and economicsLawPolitical scienceEconomicsFinance
DOInot available

Abstract

fetched live from OpenAlex

Although many people believe that the role of business is to maximize shareholder value, corporate executives and board members can no longer ignore their companies' human rights impacts on other stakeholders. Over the past four years, the role and responsibility of non-state actors such as multinationals has come under increased scrutiny. In 2011, the United Nations Human Rights Council unanimously endorsed the "UN Guiding Principles on Business and Human Rights," which outline the State duty to protect human rights, the corporate responsibility to respect human rights, and both the State and corporations' duties to provide remedies to parties. The Guiding Principles do not bind corporations, but dozens of countries, including the United States, are now working on National Action Plans to comply with their own duties, which include drafting regulations and incentives for companies. In 2014, the UN Human Rights Council passed a resolution to begin the process of developing a binding treaty on business and human rights. Separately, in an effort to address information asymmetries, lawmakers in the United States, Canada, Europe, and California have passed human rights disclosure legislation. Finally, dozens of stock exchanges have imposed either mandatory or voluntary non-financial disclosure requirements, in sync with the UN Principles. Despite various forms of disclosure mandates, these efforts do not work. The conflict lies within the flawed premise that, armed with specific information addressing human rights, consumers and investors will either reward "ethical" corporate behavior, or punish firms with poor human rights records. However, evidence shows that disclosures generally fail to change behavior because: (1) there are too many of them; (2) stakeholders suffer from disclosure overload; and (3) not enough consumers or investors penalize companies by boycotting 2015] Disclosing Disclosure's Defects 85 products or divesting. In this Article, I examine corporate social contract theory, normative business ethics, and the failure of stakeholders to utilize disclosures to punish those firms that breach the social contract. I propose that both stakeholders and companies view corporate actions through an ethical lens, and offer an eight factor test to provide guidance using current disclosures or stakeholder-specific inquiries. I conclude that disclosure for the sake of transparency, without more, will not lead to meaningful change regarding human rights impacts.

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)0.041
metaresearch head score (Gemma)0.178
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: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.215

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0410.178
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0030.010
Scholarly communication0.0100.018
Open science0.0030.008
Research integrity0.0060.009
Insufficient payload (model declined to judge)0.0050.000

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 head0.063
GPT teacher head0.226
Teacher spread0.163 · 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

Citations2
Published2015
Admission routes1
Has abstractyes

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