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Record W2998522375 · doi:10.5430/rwe.v10n5p104

Economic Literacy: Does It Matter for Policy Understanding?

2019· article· en· W2998522375 on OpenAlexvenueno aff
Ramlee Ismail, Mohd Yahya Mohd Hussin, Fidlizan Muhammad

Bibliographic record

VenueResearch in World Economy · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsHuman capitalSample (material)LiteracyEconomics educationEconomicsQuality (philosophy)PopulationEconomic statisticsEconomic growthPublic economicsSociologyHigher education

Abstract

fetched live from OpenAlex

The quality of human capital is indispensable for economic growth and sustainability. The developed nations have shown evidence of a positive relationship between education and economic development. In all respects, a better understanding of economics among citizens has led to more efficiency in implementing economic policies. In this paper, we explore a possible relationship between economic literacy and policy understanding. Policy knowledge, interest and explanation are measured through policy understanding. This study used the students’ teachers as a sample and found that economic literacy was below 50 per cent. Meanwhile, the level of policy understanding was moderate. Interestingly, our findings showed that economic literacy is not strongly associated with economic policy understanding. Policy interest appeared as an important element for policy understanding among the sample. In a volatile economic environment, the level of economic knowledge among the population is a vital factor for the implementation of economic policies. A further investigation must be conducted to assess this issue.

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)0.004
metaresearch head score (Gemma)0.040
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.010
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.040
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0100.001

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 head0.056
GPT teacher head0.345
Teacher spread0.289 · 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 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
Published2019
Admission routes1
Has abstractyes

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