Bibliographic record
Abstract
This article will discussed about demographic issues in Malaysia and focused about fertility trend, increasing the number of ageing (60 years and above) and non citizen residents. Since 2016 years the crude birth rate in Malaysia are decreased to 16.6 per 1000 population compared to 18.5 per 1000 population in 2009. Starting on years 2019, fertility trend among women are decreased which is below the replacement level 2.1 child per women (15-49 years reproductive women) especially Chinese and Indian ethnic. In 2014, Chinese ethnic and Indian showed that total fertility rate is 1.4 child per women. In 2015, Malay ethnic showed the total fertility rate is 2.6 per child among women 15- 49 years. This situation showed the issued of age population increasingly significantly. The percentage of older person in Malaysia in 2010 among Chinese, Indian, Malay and Bumiputera ethnic are representively 12.2 percent, 7.9 percent, 7.3 percent and 6.2 percent. It showed that the issues of social support for example the living arrangement of older persons, health care, health status and income are the most important issues and must be addressed. Besides that, Malaysia will be challenging problem migrant worker from Asian country and will cause the problem for example for Malaysian citizen to get the work. Based on Department of Statistics Malaysia in 2016, there are about 3.3 million non-Malaysian citizens in Malaysia which is 80.3 percent in 15-64 years. Hopefully this paper will provide some suggestions to enable the authorities to address these issues more effectively.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".