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Record W2027130752 · doi:10.5539/ijef.v1n2p170

Socio-Economic Status and Parental Savings for Higher Education among Malaysian Bumiputera Families

2009· article· en· W2027130752 on OpenAlexvenueno aff
Nor Rashidah Zainal, Rohana Kamaruddin, Siti Badariah Saiful Nathan

Bibliographic record

VenueInternational Journal of Economics and Finance · 2009
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsSocioeconomic statusIncentiveGovernment (linguistics)Family incomeHigher educationFinanceBusinessEconomic growthPsychologyEconomicsSociologyDemography

Abstract

fetched live from OpenAlex

Socioeconomic status of a family is a benchmark for a student in Malaysia to get financial aid in education, on top of their academic performance. As the number of students obtaining good grades in their Sijil Pelajaran Malaysia examination increases, entry to public universities become more competitive and the chances to get a full education financing become smaller. Most students resort to loans provided by PTPTN as many still do not have any form of saving to finance their higher education. This study attempts to explore on parental saving for children’s higher education among Bumiputera across different socio-economic groups. A survey was conducted in UiTM and six of its affiliated colleges, with the total respondents of 371. Questionnaires to parents were distributed through the students. The results of the study reveal that only 15% of the students sampled received a form of financial aid from the government. The findings also show high correlation between the socio-economic status of parents and the level of awareness towards saving for their children. The findings are hoped to create awareness in the society that saving incentives for higher education can be utilized by all low and middle-income families in all communities.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.200
Threshold uncertainty score0.460

Codex and Gemma teacher scores by category

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

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 head0.008
GPT teacher head0.226
Teacher spread0.218 · 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

Citations9
Published2009
Admission routes1
Has abstractyes

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