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Record W4213170993 · doi:10.53967/cje-rce.v44i2.4249

Financial Literacy Education in Ontario: An Exploratory Study of Elementary Teachers’ Perceptions, Attitudes, and Practices

2021· article· en· W4213170993 on OpenAlexaffvenueabout
Gail E. Henderson, Pamela Beach, Andrew Coombs

Bibliographic record

VenueCanadian Journal of Education / Revue canadienne de l éducation · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsQueen's University
Fundersnot available
KeywordsFinancial literacyCurriculumExploratory researchLiteracyPsychologyPedagogyMedical educationMathematics educationFinanceBusinessSociologyMedicine

Abstract

fetched live from OpenAlex

Politicians are pushing school boards to do more to ensure students leave school with the financial literacy skills they will need to navigate an increasingly complex financial marketplace. Financial literacy education must start early to achieve this goal, yet there has been very little Canadian research on financial literacy education at the elementary level. This exploratory study used an anonymous, online survey to gain a preliminary understanding of full-time Ontario elementary teachers’ perceptions, attitudes, and practices with respect to financial literacy education. Respondents overwhelmingly favour teaching financial literacy in elementary school. Almost half of respondents currently incorporate financial literacy into their classroom practice. These teachers rely primarily on free, online resources. With respect to barriers to teaching financial literacy, respondents cited the lack of an appropriate curriculum and lack of support from schools and school boards. Respondents identified professional development as the main type of support they would like to see schools and school boards provide to support them in teaching financial literacy going forward. Keywords: financial literacy, financial education, elementary teachers

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)0.001
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 score0.336
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.003
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.037
GPT teacher head0.299
Teacher spread0.262 · 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

Citations19
Published2021
Admission routes3
Has abstractyes

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Same venueCanadian Journal of Education / Revue canadienne de l éducationSame topicFinancial Literacy, Pension, Retirement AnalysisFrench-language works237,207