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Record W4405337985 · doi:10.58812/wsaf.v2i03.1265

Developing a national financial education strategy towards the goal of inclusive and sustainable financial development: International experience and implications for Vietnam

2024· article· en· W4405337985 on OpenAlexaboutno aff

Bibliographic record

VenueWest Science Accounting and Finance · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessFinanceSustainable developmentFinancial literacyEconomic growthPolitical scienceEconomics

Abstract

fetched live from OpenAlex

This study was conducted to examine the important role of financial education in promoting financial inclusion and sustainable development. By analyzing experiences from successful countries such as Australia, Canada, and the United Kingdom. At the same time, based on the results of semi-structured interviews with 15 subjects, including policymakers, educators, financial service providers and other stakeholders to explore their experiences and perspectives on national financial education strategies towards inclusive and sustainable financial development. The results of the study identify valuable experiences for Vietnam such as effective government leadership, collaborative approaches, lifelong learning strategies, and targeted programs for vulnerable groups. Based on the results of the study, the proposed recommendations include establishing a centralized authority to coordinate activities, mobilizing resources through public-private partnerships, adopting community-based approaches, and developing curriculum appropriate to the context. By implementing the proposed recommendations, Viet Nam can improve the financial literacy and welfare of its people, promote financial inclusion, and contribute to the broader development goals.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0040.002
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.017
GPT teacher head0.295
Teacher spread0.278 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations0
Published2024
Admission routes1
Has abstractyes

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