Disclosure Quality on Governance Issues in Annual Reports of Malaysian PLCs
Bibliographic record
Abstract
This paper investigates the disclosure quality of governance issues in annual reports of Malaysian PLCs. In recent years, the issue of corporate governance (CG) has received more attention than it would ordinarily have as a result of a series of corporate failures. Corporate collapses like Enron Corporation (US), Barings Empire (UK) and in Malaysia cases such as Perwaja and Pan Electric Inc. are all rooted in the lack of a proper governance system. As a result, the Finance Committee on Corporate Governance was established in 1998 to undertake a review of the legal and regulatory infrastructure, specifically to evaluate its effectiveness in promoting sound CG standards in Malaysia. Following this development, a few guidelines on CG have been released, particularly addressing the principles and best practices such as the Malaysian Code of Corporate Governance (the Code), the Capital Market Master Plan, and the Financial Sector Master Plan. The main purpose of these guidelines is to strengthen CG standards and practices in Malaysia by focusing on the role and responsibilities of various CG participants, mainly the management, board of directors, audit committee (AC), external and internal auditors. Secondary data is used in this study. A disclosure index is established following the Bursa Malaysia Governance Model, the Code’s guidelines and Committee of Sponsoring Organizations of the Treadway Commission (COSO) guidelines. This study also aims to examine factors that possibly affect both the quality and quantity of disclosure. In general, it may be concluded that Malaysian companies have complied with the standards requirements. Only three factors under observation namely leverage, size and type of industry were found to have relationship with the quality of disclosure relates to governance issues.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.160 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".