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Record W3168381718 · doi:10.5539/ijef.v13n7p7

Financial Literacy, Stability, and Security as Understood by Male Saudi University Students

2021· article· en· W3168381718 on OpenAlexvenueno aff
Amani K. Hamdan Alghamdi, Sue L. T. McGregor, Wai Si El-Hassan

Bibliographic record

VenueInternational Journal of Economics and Finance · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsFinancial literacyFinanceFinancial stabilityFinancial planDescriptive statisticsRetirement planningBusinessLiteracyEconomicsPublic relationsSociologyAccountingPolitical scienceEconomic growthFinancial system

Abstract

fetched live from OpenAlex

This paper recounts an inaugural study of male Saudi university students’ understandings of financial literacy, financial stability, and financial security and how they plan to achieve these. Using convenience sampling, 79 male respondents (53% response rate) from an Eastern Province university completed a six-question open-ended email instrument. Data collected in November 2020 were analyzed using descriptive statistics. Results showed that while their understanding of what constitutes financial literacy was solid enough (with some gaps), their notion of how to ensure financial stability and security was in question. They made no mention of retirement, taxation, or estate planning and limited insurance to medical. Despite self-rating themselves as having good (47%) or average (32%) financial literacy, results suggest an imbalanced personal financial system, which bodes ill for future financial resilience, stability, and security. Respondents placed an inordinate weight on the risky ventures of investing (79%) and entrepreneurship (49%) to make a living and to use for retirement while concurrently not valuing goal setting, budgeting, or funding emergencies. Virtually all (99%) respondents said they planned to learn more about financial literacy, and they tendered an array of ideas for how the university could make this happen.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.700
Threshold uncertainty score0.513

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 head0.009
GPT teacher head0.220
Teacher spread0.211 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Economics and FinanceSame topicFinancial Literacy, Pension, Retirement AnalysisFrench-language works237,207