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Record W4205100386 · doi:10.11118/lifele20211103243

Comparison of Financial Literacy Concept in Projected Curricula of Selected Countries

2021· article· en· W4205100386 on OpenAlexaboutno aff
Karel Ševčík

Bibliographic record

VenueLifelong Learning · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsFinancial literacyCurriculumCzechScope (computer science)Political scienceLiteracyPoliticsPedagogyMathematics educationSociologyPublic relationsFinancePsychologyBusinessComputer science

Abstract

fetched live from OpenAlex

Due to the turbulent economic development in recent years and the rising number of financial issues individuals need to deal with, financial literacy is becoming a widely recognized concept, which, among others, spreads into the field of primary and secondary education. However, in each country, the different political, social, or cultural environment influences the final form of concept implementation. Therefore, the presented study focuses on the analysis and comparison of project curricula, which are crucial documents for financial education at the primary levels. A categorical system was presented as the main research tool, with the purpose to examine documents from the countries of the USA (Utah), Canada (Ontario), the Czech Republic and Australia. The results suggested the relative disunity of the financial literacy concept within the Ontario curriculum, as no comprehensive content block is devoted to it during the study. The concept is presented only as one of the cross-curricular topics, often lacking any continuity. Within the Utah curriculum, the individual actions should be cognitively more demanding so that the students are properly stimulated to be active and solve the given task. The Czech curriculum then suffers from a lack of guidance in the curriculum concerning financial literacy. The cause might be in the lower scope of the document.

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)0.002
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 score0.035
Threshold uncertainty score0.866

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.003
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 head0.012
GPT teacher head0.280
Teacher spread0.268 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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