Paradigm shift from a liquidation culture to a corporate rescue culture in Malaysia: A legal review
Bibliographic record
Abstract
Abstract The company law landscape in Malaysia has witnessed a significant change in its insolvency law with the adoption of two new corporate rescue mechanisms, the corporate voluntary arrangement and judicial management under the Companies Act 2016 (CA 2016), which has repealed the Companies Act 1965 (CA 1965). Previously, the insolvency laws under the CA 1965 were based on the traditional pro‐creditor laws of winding up and receivership, which embodied the liquidation culture. This article examines the transition of the insolvency laws in Malaysia from a liquidation culture under the CA 1965 to a corporate rescue culture under the CA 2016. It also reviews the necessary changes to the pro‐creditor laws, which are preserved under the CA 2016 in order to accommodate the pro‐debtor laws with the introduction of the corporate rescue mechanisms, which came into force on March 1, 2018. Through comparative and critical analysis of similar laws in the United Kingdom and Singapore, this article argues that while the corporate rescue mechanisms are regarded as pro‐debtor however the review reveals that the position of secured creditors are impeding its application and reforms ought to be considered.
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 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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".