MétaCan
Menu
Back to cohort
Record W4206324927 · doi:10.1080/23311886.2021.1996919

Financial knowledge, financial confidence and learning capacity on financial behavior: a Canadian study

2022· article· en· W4206324927 on OpenAlexaffabout
Tania Morris, Stéphanie Maillet, Vivi Koffi

Bibliographic record

VenueCogent Social Sciences · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsUniversité de Moncton
Fundersnot available
KeywordsFinancial literacyFinanceFinancial ratioFinancial modelingFinancial analysisStructural equation modelingSample (material)BusinessComputer science

Abstract

fetched live from OpenAlex

This study examines the relationships between financial knowledge, confidence, learning capacity, education and other sociodemographic information and financial behavior. A structural equation model is used to analyze the relationships between the study variables and to obtain a more comprehensive understanding of the factors linked to poor financial behavior among a large Canadian sample. The main findings showed that financial confidence plays a crucial role in explaining financial behavior and that learning capacity explains financial confidence. Overall, our results suggest that financial education should be considerably improved and that additional focus should be placed on financial confidence and individual’s learning capacity in order to mitigate existing financial difficulties, prevent new problems from arising, and develop and implement constructive strategies to achieve specific financial goals. This study contributes to previous research on financial literacy by demonstrating the influence of learning capacity on financial confidence and financial behavior.

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)0.006
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 score0.029
Threshold uncertainty score0.213

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.005
Science and technology studies0.0070.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.047
GPT teacher head0.276
Teacher spread0.229 · 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

Citations45
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueCogent Social SciencesSame topicFinancial Literacy, Pension, Retirement AnalysisFrench-language works237,207