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Record W4281289330 · doi:10.3390/jrfm15050228

Understanding Post-Privatisation Performance of Statutory Bodies Subject to Government Shareholding—A Suggested Theoretical Framework, for Malaysian Researchers

2022· article· en· W4281289330 on OpenAlexvenueno aff
Philip Sinnadurai, Susela Devi

Bibliographic record

VenueJournal of risk and financial management · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsnot available
FundersMacquarie University
KeywordsStatutory lawShareholderGovernment (linguistics)Agency (philosophy)Principal–agent problemSubject (documents)BusinessPrincipal (computer security)Private sectorEmpirical researchAccountingEconomicsPublic economicsCorporate governanceFinancePolitical scienceSociologyLawEconomic growthComputer scienceSocial science

Abstract

fetched live from OpenAlex

The purpose of this concept paper is to suggest a theoretical framework for understanding the post-privatisation performance of statutory bodies, subject to government shareholding. We identify a suitable model, from the analytical economics literature. We argue that this model is a manifestation of agency theory. Our proposed framework for using this theory is replete with examples from Malaysia. We conclude that in Malaysia, the principal determinant of whether government subsidisation enhances or erodes shareholder wealth is the level of government shareholding. We also predict that in Malaysia, the relation between shareholder wealth and government shareholding follows an “inverted U” shape. However, the turning is likely to vary, cross-sectionally and temporally. We believe that the framework presented within this paper can be used to understand empirical results reported by other Malaysian studies into the shareholder wealth effects arising from economic policies featuring close co-operation between the public and private sectors.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0040.005
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.046
GPT teacher head0.245
Teacher spread0.200 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2022
Admission routes1
Has abstractyes

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