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Muddling through Political and Economic Tensions:A Territorialized Conception of Political CSR

2014· article· en· W2058413565 on OpenAlexaff
Nolywé Delannon, Emmanuel Raufflet

Bibliographic record

VenueAcademy of Management Proceedings · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicState Capitalism and Financial Governance
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsPoliticsCorporate social responsibilityContext (archaeology)CorporationConversePolitical scienceOrder (exchange)SociologyPublic sphereSocial movementPolitical economyPublic relationsBusinessLawEpistemology

Abstract

fetched live from OpenAlex

This article draws on the political CSR literature to investigate how an organization responds to political and economic tensions through its corporate community involvement (CCI). It is based on the longitudinal case study of a hybrid organization in the space industry, which has engaged in relations with the local community over the last 50 years and has gone through significant change. Through this exploratory study, we make two main contributions. Firstly, we extend the political CSR literature by showing that the current transformation of CSR is not only the result of a movement of the private corporation into the public (political) sphere, but also the consequence of a converse movement of public (political) actors into the economic sphere. The blurred frontiers between the economic and political spheres theorized by political CSR are then the result of this dynamic and nonlinear movement, which increasingly generates hybrid organizations. Secondly, this paper shows that in order to understand how CSR is shaped it is necessary to recognize the contingencies of the local context in which a firm operates. This finding critically challenges the de-territorialized approach of political CSR which implicitly leads to the conclusion that CSR follows a universal template, and instead calls for more research on the role of the local context (i.e. the community) in shaping 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)0.000
metaresearch head score (Gemma)0.000
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.560
Threshold uncertainty score0.719

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.024
GPT teacher head0.259
Teacher spread0.235 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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