A legal study on the crime of infanticide in Malaysia / Ungku Masmera Ungku Farouk ...[et al.]
Bibliographic record
Abstract
Nowadays, the problem of infanticide or killing of a new-born babies is a major concern in our country. Eventhough there are several provisions available to regulate this problem, yet there are still cases regarding infanticide on the rise. The psychosocial factors and the flaws in the existing law regarding infanticide are two main contributory factors that lead to this problem. In Malaysia, the provisions on infanticide are available in Penal Code which are Section 3 09A and Section 309B for the offence of infanticide and Section 318 for the concealment of the dead body of the babies. However, there are several flaws in these sections that render the implimentation of the law to be less efficient. Besides that, most of the countries have their own laws regarding the offence of infanticide, for example, the Infanticide Act 1938 in United Kingdom, and in Canada the provisions regarding this offence are available in the Criminal Code of Canada. Therefore, this study will highlights the causes of infanticide, the existing laws together with the flaws in it. It also higlights the similarities and the differences between the laws in Malaysia, United Kingdom and Canada. This study provides some suggestions to improve the existing laws regarding infanticide in Malaysia.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".