MétaCan
Menu
Back to cohort
Record W4387974610 · doi:10.1111/1467-8551.12771

The Governance Role of Minority State Ownership in Non‐state‐owned Enterprises: Evidence from Corporate Fraud in China

2023· article· en· W4387974610 on OpenAlex

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

VenueBritish Journal of Management · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicCorruption and Economic Development
Canadian institutionsMcMaster University
FundersNational Office for Philosophy and Social SciencesNatural Science Foundation of Sichuan ProvinceJilin Office of Philosophy and Social ScienceChina Postdoctoral Science Foundation
KeywordsCorporate governanceBusinessState ownershipShareholderChinaIncentiveContext (archaeology)AccountingState (computer science)Government (linguistics)PoliticsIntervention (counseling)Control (management)Emerging marketsMarket economyEconomicsFinancePolitical scienceLaw

Abstract

fetched live from OpenAlex

Abstract This study attempts to shed new light on how the state as a minority shareholder benefits stakeholders, by investigating its role in deterring corporate fraud in non‐state‐owned enterprises (non‐SOEs). Through an analysis of publicly traded non‐SOEs in China, this study reveals that minority state ownership negatively impacts firm fraud, and the results hold after alternative tests. The identified channels of this association are that minority state ownership mitigates tunnelling, enhances internal control, and alleviates the financial constraints of non‐SOEs. Further analysis shows that this relationship is more pronounced in firms with weaker corporate governance, stronger fraud incentives, and lower levels of political connections. Overall, this study contributes to our understanding of the role of minority state ownership in emerging markets within the context of corporate fraud, highlighting the importance of critically evaluating the effects of government intervention in different contexts.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.790
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 head0.025
GPT teacher head0.258
Teacher spread0.233 · 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