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Control Modes and Outcomes of Transformed State-Owned Enterprises in China: An Empirical Test

2011· article· en· W2138874151 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

VenueManagement and Organization Review · 2011
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsYork University
Fundersnot available
KeywordsChinaControl (management)Agency (philosophy)State (computer science)Transformation (genetics)Economic systemBusinessTransition (genetics)Agency costIndustrial organizationPrincipal–agent problemMarket economyEconomicsManagementFinancePolitical scienceCorporate governanceLaw

Abstract

fetched live from OpenAlex

The transformation of state-owned enterprises (SOEs) into efficient entities has been an important approach in transition economies. However, the transition literature reveals little about how control structure affects firm performance of transformed SOEs. Drawing on agency theory, we distinguish three modes of control in transformed SOEs: state-controlled, dispersedly controlled, and privately controlled modes and argue that actual control after transformation plays a critical role in determining performance. Examining the impact of different control modes in China, we find that the key is who controls the transformed firm. Non-state-controlled (dispersedly controlled and privately controlled) firms are more likely to have enhanced post-transformation performance and reduced agency costs than state-controlled firms.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.429

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 head0.016
GPT teacher head0.227
Teacher spread0.210 · 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