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Legacies of Logics: Sources of Community Variation in CSR Implementation in China

2013· article· en· W7135386917 on OpenAlexaff
Mia Raynard, Michael Lounsbury, Royston Greenwood

Bibliographic record

VenueWU Research · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicChina's Socioeconomic Reforms and Governance
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCorporate social responsibilityChinaVariation (astronomy)PhenomenonContext (archaeology)State (computer science)Institutional theoryComparative research

Abstract

fetched live from OpenAlex

This paper explores how legacies of past logics spawn variation in the institutional landscapes of different geographic regions in China. Of particular interest is how this variation influences the ways that actors interpret and respond to broader societal and world society pressures. Employing a cross-level comparative research design, we examine the enduring legacies of previous state logics, which have given rise to distinctive material and symbolic resource environments in different regional communities across China. To the extent that institutional contexts direct the attention of actors toward particular environmental stimuli and provide the symbolic and material resources to respond, a better understanding of how contexts differ provides more accurate causal explanations of the variability of organizational behavior. We explore this phenomenon in the context of recent government-mandated corporate social responsibility (CSR) initiatives in China. Our examination of public and private CSR initiatives, along with the CSR activities of a sample of 714 listed Chinese companies, suggests that legacies from past state logics become embedded in local institutional infrastructures and shape how abstract, multifaceted CSR initiatives are interpreted and implemented.

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)0.004
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.467
Threshold uncertainty score0.896

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.082
GPT teacher head0.439
Teacher spread0.357 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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