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Record W3092204949

Mandatory Corporate Social Responsibility Legislation around the World: Emergent Varieties and National Experiences

2020· article· en· W3092204949 on OpenAlexaff
Li-Wen Lin

Bibliographic record

VenueeYLS (Yale Law School) · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCorporate social responsibilityLegislationMandateGreenwashingLegislaturePoliticsPolitical scienceStakeholderLaw and economicsFunction (biology)BusinessCorporate governancePublic relationsLawEconomics
DOInot available

Abstract

fetched live from OpenAlex

Corporate social responsibility (CSR) is typically assumed as a voluntary initiative rather than a legal mandate.Yet, in recent years, a growing number of countries have adopted laws that explicitly require corporations to undertake CSR.When it comes to CSR legislation, most scholars focus on mandatory disclosure.This article presents emergent varieties of CSR legislation other than mandatory disclosure and investigates the experiences of representative adopting countries.It compares and evaluates the motivation, nature, implementation, function and potential diffusion patterns of the emerging types of CSR legislation.This article shows that while the new legislative methods appear progressive, politics and the open-ended notion of CSR significantly weaken the compulsory nature of the laws.The major function of the CSR laws as they currently stand appears mostly expressive.At best, the explicit recognition of CSR in the laws may send signals about appropriate corporate behavior and reconstruct business norms that exclusively focus on profits.At worst, the laws may be political greenwashing through which politicians give symbolic importance to CSR.This article offers policy lessons and possible directions for future reform.1. See generally

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)0.002
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.581
Threshold uncertainty score0.855

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
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 head0.054
GPT teacher head0.281
Teacher spread0.227 · 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

Citations19
Published2020
Admission routes1
Has abstractyes

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