MétaCan
Menu
Back to cohort
Record W2981863111 · doi:10.5539/ass.v15n11p116

Demographic Issues in Malaysia

2019· article· en· W2981863111 on OpenAlexvenueno aff
Nik Norliati Fitri Md Nor

Bibliographic record

VenueAsian Social Science · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicDemographic Trends and Gender Preferences
Canadian institutionsnot available
FundersUniversiti Sains Malaysia
KeywordsMalayEthnic groupFertilityTotal fertility ratePopulationDemographyBirth rateSocioeconomicsPopulation ageingGeographyFamily planningSociologyResearch methodology

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.627
Threshold uncertainty score0.561

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.004
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.021
GPT teacher head0.316
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueAsian Social ScienceSame topicDemographic Trends and Gender PreferencesFrench-language works237,207