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Record W4402370621 · doi:10.3390/jrfm17090404

Digital Financial Capability Scale

2024· article· en· W4402370621 on OpenAlexvenueno aff
Kelmara Mendes Vieira, Taiane Keila Matheis, Eliete dos Reis Lehnhart

Bibliographic record

VenueJournal of risk and financial management · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsnot available
FundersConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsScale (ratio)DigitizationConfirmatory factor analysisFinanceFinancial servicesFinancial modelingConstruct (python library)Exploratory factor analysisComputer scienceStructural equation modelingKnowledge managementBusinessMachine learningGeography

Abstract

fetched live from OpenAlex

Financial digitization is an irreversible phenomenon. The objective of this study is to construct the Digital Financial Capability Scale (DFCS). Starting with the development of a definition, we created a multidimensional scale composed of digital financial knowledge, digital financial behavior, and digital financial confidence. The validation process involved a qualitative stage, consisting of focus groups, expert validation, and pre-testing, and a quantitative stage, with exploratory and confirmatory factor analyses and structural equation modeling. The DFCS assesses an individual’s perception of their ability to apply financial knowledge, adopt appropriate financial behaviors, and feel confident in making financial decisions in a digital environment. The final version of the DFCS consists of a set of 33 items divided into the three dimensions. The scale can be very useful for researchers who wish to study financial capability in the digital environment, for financial agents to evaluate clients, and for assessing the outcomes of public policies aimed at enhancing the financial capability of the population.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.614
Threshold uncertainty score0.938

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
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 head0.005
GPT teacher head0.198
Teacher spread0.193 · 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

Citations8
Published2024
Admission routes1
Has abstractyes

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