A comparative legal study on discharge of bankruptcy in Malaysia, United Kingdom, Australia, and Canada / Rehah Ismail
Bibliographic record
Abstract
The Malaysian economy is facing another challenging year in 2020 due to COVID-19 pandemic. COVID-19 has exposed financial institutions to an increase in non-performing loan or credit default risk due to a high number of business closures and increase in unemployment. The closure of businesses would lead to cash flow problems which in turn will affect individual’s capability to fulfill their financial instalments. Bankruptcy cases are expected to increase tremendously. The effort by Malaysian government in increasing the threshold amount to RM100,000 by virtue of the Insolvency (Amendment Act) 2020 in order to reduce the volume of bankruptcy cases is applauded. However, the other provisions in the Insolvency Act 1967 including automatic discharge of bankruptcy remain unchanged despite the current pandemic situation. This research intends to examine the effectiveness of automatic discharge provision under the Insolvency Act 1967 governing personal insolvency in Malaysia. This research employs a qualitative method in identifying and analysing the existing law and any legal issues and problems derive from the discharge provision in Malaysia and other countries such as United Kingdom, Australia, and Canada to determine the effectiveness of the law and whether it requires legal reform as well. This research will propose recommendation to overcome any legal issues and problems of its implementation.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".