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Record W4306399406 · doi:10.1111/ajfs.12393

Political Uncertainty and Corporate Social Responsibility in a Transition Economy

2022· article· en· W4306399406 on OpenAlex
Maoyong Cheng, Yuxuan Dong, Justin Yiqiang Jin, Kiridaran Kanagaretnam

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

VenueAsia-Pacific Journal of Financial Studies · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsYork UniversityMcMaster University
Fundersnot available
KeywordsCorporate social responsibilityPoliticsGovernment (linguistics)Language changeBusinessTurnoverChinaMarket economyPolitical economyEconomicsPolitical sciencePublic relationsLawManagement

Abstract

fetched live from OpenAlex

Abstract We examine the influence of political uncertainty on the corporate social responsibility (CSR) of local firms in China. Political uncertainty refers to government officials' turnover. We find that these firms significantly increase their CSR activities when city government officials are changed or replaced. We also find that political uncertainty increases firms' attention to employee responsibilities, supply chain responsibilities, and environmental responsibilities. In addition, the turnover of government officials increases CSR activities due to the reduction or loss of political connections. The anti‐corruption campaign has also strengthened the influence of political uncertainty on 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.

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)0.002
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.132
Threshold uncertainty score0.746

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.049
GPT teacher head0.279
Teacher spread0.231 · 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