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.
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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".