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Record W4415873496 · doi:10.1177/07349149251384167

Newcomers’ Financial Knowledge and Saving Participation in Canada: A Social Equity Gap Analysis

2025· article· en· W4415873496 on OpenAlexaffabout
Gino Biaou

Bibliographic record

VenuePublic Administration Quarterly · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsUniversité du Québec en OutaouaisÉcole Nationale d'Administration Publique
Fundersnot available
KeywordsFinancial literacyEquity (law)Settlement (finance)Agency (philosophy)ImmigrationFinancial analysisFinancial servicesSavings accountCitizenship

Abstract

fetched live from OpenAlex

Using the 2019 Canadian Financial Capability Survey (CFCS), this study examines whether social equity gaps persist in financial knowledge and retirement savings participation between newcomers and those born in Canada. Compared to well-established immigrants and their Canadian-born counterparts, the results suggest that newcomers have significantly lower levels of financial knowledge and are less likely to participate in registered retirement savings plans. To catalyze actions for better financial outcomes for newcomers in Canada, the Financial Consumer Agency of Canada could consider establishing a Financial Literacy Working Group for Newcomers (like the one for Indigenous Peoples). Furthermore, to reduce the information gap, Immigration, Refugees, and Citizenship Canada could add a “Financial Education Courses” category box on the settlement services Canada.ca webpage, which would help newcomers easily filter and find settlement organizations offering financial literacy services.

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)0.001
metaresearch head score (Gemma)0.005
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 score0.030
Threshold uncertainty score0.219

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.007
Science and technology studies0.0050.001
Scholarly communication0.0020.001
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.033
GPT teacher head0.307
Teacher spread0.275 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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