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Record W3184550356 · doi:10.1111/beer.12378

Impeding corporate social responsibility: Revisiting the role of government in shaping business — Marginalized local community relations

2021· article· en· W3184550356 on OpenAlexafffund
Nolywé Delannon, Emmanuel Raufflet

Bibliographic record

VenueBusiness Ethics the Environment & Responsibility · 2021
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsHEC MontréalUniversité Laval
FundersFonds de Recherche du Québec-Société et Culture
KeywordsCorporate social responsibilityConceptualizationPoliticsGovernment (linguistics)TypologyPublic relationsPolitical scienceState (computer science)SociologyBusinessLaw

Abstract

fetched live from OpenAlex

Abstract This paper is based on a case study of the European space industry as it is organized in the postcolonial setting of French Guiana. It brings the state back into political corporate social responsibility (CSR) by showing how government shapes interactions between business and local communities, more specifically around CSR issues. The paper opens new research avenues in political CSR by making two significant contributions. First, it identifies postcolonial contexts as instances in which government, rather than stepping back, gets actively involved to impede the emergence of CSR. This demonstration is made by building on a conceptualization of the marginalized local community as comprising citizens—rather than mere stakeholders—who expect their government to defend their rights vis‐à‐vis business. Second, by using a longitudinal approach that provides access to dynamics of interactions over time, the paper develops a typology of mechanisms that support a government’s changing role in CSR. Over a 50‐year period, such government is shown to have played the roles of impeding the emergence of CSR, partnering for CSR, symbolically mandating CSR, and finally, disengaging from CSR.

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)0.012
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.835
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.002
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 head0.061
GPT teacher head0.254
Teacher spread0.194 · 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 designObservational
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

Citations8
Published2021
Admission routes2
Has abstractyes

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