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Record W2944919734 · doi:10.5539/jms.v9n1p159

The Influence of Gender and Ethnicity on Young Adults’ Participation in Financial Education Programme

2019· article· en· W2944919734 on OpenAlexvenueno aff
Mohamad Fazli Sabri, Rusitha Wijekoon

Bibliographic record

VenueJournal of Management and Sustainability · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsFinancial literacyMalayEthnic groupEmpowermentIntervention (counseling)PopulationMedical educationBusinessPsychologyEconomic growthFinancePolitical scienceMedicineSociologyDemographyEconomics

Abstract

fetched live from OpenAlex

A major problem encountered by educationalists, community leaders and policy makers is to transfer financial literacy and consumer education successfully to their community. Delivering of financial education for youth of a country is one possible intervention to improve the financial capabilities of a population. Therefore, for an effective training we have to identify their financial needs. Further they need guidance and access for financial knowledge and money management tools. Therefore, the objectives of this study are to identify the training needs of youth by gender and ethnicity about money management and to determine their interest towards it. The sample was comprised of 220 secondary school students from five schools in Greater Klang Valley/Kuala Lumpur with 112 females and 108 males and the data collection was done using self-administered questionnaire. The results shown that about one third of female youths have preferred to participate on financial literacy programs than male youths (21.5%). In addition, most of the Malay respondents said that they need more information to take efficient decisions on saving, borrowing and insurance, followed by Indians (64%) and Chinese (61.5%). The findings of this study would be used to the development of financial empowerment program of youth in Malaysia in order to enhance their financial literacy.

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)0.001
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 score0.024
Threshold uncertainty score0.341

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 head0.256
Teacher spread0.248 · 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

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