A Case Study of Financial Literacy and Debt of Immigrants in Lloydminster, Canada
Bibliographic record
Abstract
The high debt-to-income ratios and the low financial literacy levels among Canadian immigrants are hindering public welfare, macroeconomic policies, and economic growth. The purpose of this qualitative exploratory case study was to explore why immigrants to Lloydminster, Canada possess high debt-to-income ratios in their financial portfolios by examining pertinent themes and patterns between their debt profiles and their financial literacy levels. The life cycle hypothesis, rational choice theory, and bounded rationality theory grounded the study. Data collection from the purposeful sample included semistructured face-to-face interviews with 13 adult immigrants and a focus group discussion with 6 adult immigrants, all of whom lived, worked, or owned a business in the city of Lloydminster. The application of Yin's 5-step data analytic procedure revealed key findings that described the pattern between immigrants' debt profiles and their financial literacy levels including environmental curiosity, excellent credit score, family survival, rational decision making, social institutions, economic institutions, pressure impacting financial decisions, credit facility impacting financial decisions, emotions impacting financial decisions, and discount deals impacting financial decisions. Immigrants to Canada can utilize the findings from this study to develop their financial literacy levels and stay committed to making sensible financial decisions, thus triggering positive social change. Sound spending habits could have positive implications for Canada's Gross Domestic Product growth and immigrants' wellbeing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".