Examining the financial knowledge of immigrants in Canada: a new dimension of economic inequality
Bibliographic record
Abstract
Immigrant populations in developed societies face challenges to economic integration and are at high risk for financial precarity. Both structural and individual factors may contribute to the precarious financial lives of immigrants. Financial knowledge, which refers to individuals’ understanding of everyday finances, is considered one of the individual-level factors that influence individuals’ financial well-being. To date, limited work has examined the financial knowledge of immigrant populations. In this study, we used data from the 2009 and 2014 Canadian Financial Capability Survey (N = 22,204) to understand the levels of financial knowledge of immigrants. Our findings suggest that immigrants have significantly lower levels of financial knowledge than their Canadian-born counterparts. Among immigrants, those with a shorter stay in Canada have significantly lower levels of financial knowledge compared to those with a longer stay. Some other groups, such as older adults, females, and those with lower levels of education and income have lower levels of financial knowledge than other groups, which puts them at higher risk for financial fraud, abuse, and exploitation. Building financial knowledge and adopting inclusionary financial policies will help protect immigrant populations from financial vulnerability and enhance their financial well-being.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".