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Record W4206172509 · doi:10.33516/rb.v46i1-2.1-16p

Corporate Governance in Major Asian Economies and Canada - A Critical Study

2020· article· en· W4206172509 on OpenAlexaboutno aff
Padmanabhan Satyes Kumar

Bibliographic record

VenueResearch Bulletin · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicRisk Management in Financial Firms
Canadian institutionsnot available
Fundersnot available
KeywordsLaggingCorporate governanceChinaBenchmark (surveying)Best practiceAccountingBusinessAsian studiesProcess (computing)EconomicsDevelopment economicsEconomyPolitical scienceFinanceGeographyManagement

Abstract

fetched live from OpenAlex

Good governance is crucial for real growth of any organization. Hence its actual implementation in the major Asian economies (China, Japan and India) is critically reviewed with reference to some international benchmark practices (Singapore and Canada). While Japan’s seems to be lagging behind India in current evaluations, China’s score is quite low mainly due to some concerns on regulatory risk and inadequate implementation, though there are some good provisions in its current regulations. India on the other hand has made substantial progress ever since the SEBI regulations of 2000 but the implementation remains sketchy especially in PSUs. However with reference to the Asian and world benchmark codes, definite challenges remains for these major economies particularly in practice and implementation of the best practices. As better governance is a dynamic process of evolution, further refinements, better implementation and oversight needs to be done to better the impact for overall Business Enterprise Governance.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.091
Threshold uncertainty score0.663

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.013
Science and technology studies0.0060.003
Scholarly communication0.0070.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.067
GPT teacher head0.284
Teacher spread0.217 · 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 designQualitative
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
Published2020
Admission routes1
Has abstractyes

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