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Record W3125915223 · doi:10.5840/beq20122215

The Case for Leverage-Based Corporate Human Rights Responsibility

2012· article· en· W3125915223 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBusiness Ethics Quarterly · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Law and Human Rights
Canadian institutionsYork University
Fundersnot available
KeywordsCorporate social responsibilityLeverage (statistics)HarmHuman rightsNothingBusinessLaw and economicsSocial responsibilityBusiness ethicsMoral responsibilityPublic relationsPolitical scienceLawEconomicsEpistemology

Abstract

fetched live from OpenAlex

ABSTRACT: Should companies’ human rights responsibilities arise, in part, from their “leverage”—their ability to influence others’ actions through their relationships? Special Representative John Ruggie rejected this proposition in the United Nations Framework for business and human rights. I argue that leverage is a source of responsibility where there is a morally significant connection between the company and a rights-holder or rights-violator, the company is able to make a contribution to ameliorating the situation, it can do so at modest cost, and the threat to human rights is substantial. In such circumstances companies have a responsibility to exercise leverage even though they did nothing to contribute to the situation. Such responsibility is qualified, not categorical; graduated, not binary; context-specific; practicable; consistent with the social role of business; and not merely a negative responsibility to avoid harm but a positive responsibility to do good.

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.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.202
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0040.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.117
GPT teacher head0.297
Teacher spread0.180 · 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