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Record W604491883

The independent directors of Malaysian listed firms and their busyness

2013· article· en· W604491883 on OpenAlexaboutno aff
Shamsul Nahar Abdullah

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAccountingShareholderBusinessSample (material)Quarter (Canadian coin)Government (linguistics)Corporate governanceFinance
DOInot available

Abstract

fetched live from OpenAlex

The objective of this paper is to examine the extent to which the independent directors are independent of management and to what extent they are busy. From a sample of 200 non-finance Malaysian listed firms for the 2009 and 2010 financial years, it is observed that the almost 60% of the independent directors have professional backgrounds and another 20% are either serving or retired government servants. Hence, by virtue of their training, a majority of them are independent and objective. Findings also show that the largest shareholders of the sample firms are as follow: families (66%), government (4%); institutions (8%). On average, there are 1.5 family members or 21% of the board size. While the role of senior independent directors is very important in a concentrated ownership environment, only one quarter of the sample firms appoint senior independent directors. Out of 135 family owned firms, only one fifth appoint senior independent directors. As for board chairman, only 30% of sample firms and family owned firms appoint independent directors as the board chairman. In terms of independent directors’ busyness, on average, each independent director holds one directorship in other listed firms. All independent directors seem to attend all board meetings. Hence, the independent directors appear to be not so busy and are able to discharge their duties.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.008
GPT teacher head0.183
Teacher spread0.175 · 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 designObservational
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
Published2013
Admission routes1
Has abstractyes

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