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Record W2406059893 · doi:10.1108/jfep-06-2015-0037

Financial exclusion and financial capabilities in Canada

2016· article· en· W2406059893 on OpenAlexaffabout
Laura Lamb

Bibliographic record

VenueJournal of Financial Economic Policy · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsThompson Rivers University
Fundersnot available
KeywordsFinanceFinancial literacyMainstreamFinancial servicesGeography of financeEconomicsLoanFinancial planOriginalityBusinessFinancial analysisSociologyPolitical scienceSocial science

Abstract

fetched live from OpenAlex

Purpose The financially excluded are often denied basic financial services from mainstream banking institutions, leading them to high-cost fringe finance institutions (FFIs) such as payday loan companies and pawnshops. While strategies to address financial exclusion often include financial capabilities education, there does not appear to be evidence suggesting such education is an appropriate solution. The purpose of this study is to explore the relationship between financial capability and financial exclusion with survey data collected from the Canadian city of Kamloops located in the southern interior of British Columbia. Design/methodology/approach This exploratory research addresses the objective with survey data collected on the banking habits and financial capability levels of fringe finance users in a Canadian city. Findings The results imply that fringe finance users do not have lower levels of financial capability than those who do not use fringe finance, when education and income are controlled. Research limitations/implications Limitations include the relatively small survey sample of 105 people in one urban center in Canada. Originality/value While financial literacy is acknowledged to be an important life skill for all members of society, there is no conclusive evidence suggesting it is a solution to financial exclusion. This is the first research to examine the relationship between financial exclusion and fringe finance use in Canada by collecting data on fringe finance users with face-to-face interviews.

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)0.002
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 score0.174
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.006
GPT teacher head0.197
Teacher spread0.190 · 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

Citations22
Published2016
Admission routes2
Has abstractyes

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