Legal study of murder and culpable homicide in Malaysia: an analysis on the proof of culpability of state of mind, with reference to the United States of America and Canada. / Helmi Zaharin … [et al.]
Bibliographic record
Abstract
This research paper is centered on studying murder and culpable homicide in Malaysia, with special focus on the proof of culpability of state of mind. Apart from special analysis on Malaysian position, this paper will take a brief look too, at the position held by the United States of America and Canada. This paper will look into Model Penal Code and New York Penal Law for the law of the United States of America while for Canada, the Canada Criminal Code. The goal is to study and convey the differences between the ingredients contained here in Penal Code and what is laid in their law, with special focus on the proof of culpability of murder and culpable homicide. Basically, this paper will take a look at that element of mental states and knowledge on the Malaysian Penal Code, and then compared with what has been laid there in the United States of America and Canada's law. This has been done by studying and examining the prescribed state's law such as Model Penal Code, New York Penal Law and Canada's Criminal Code. Besides that, there are some cases from United States as well as Canada, as a legal proof from the court. Through this comparative study, this paper aim to show what is best from each law and in the end, if the law here is insufficient, this paper is hoped to be a landmark research which will provide a basis for us, Malaysian to copy what is good from them.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".