A Case-Study of Financial Literacy and Wellbeing of Immigrants in Lloydminster, Canada
Bibliographic record
Abstract
The sources of financial literacy education accessible to immigrants to Canada and the link between immigrant’s financial literacy and financial decisions could impact their welfare and Canada’s population growth negatively. The purpose of this qualitative exploratory case study was to explore sources of immigrant’s financial literacy education immediately they arrive Canada and the link between their financial knowledge and financial decisions. The life cycle hypothesis, rational choice theory, and bounded rationality theory grounded the study. Data collection from the purposeful sample included semi-structured 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. Data was collected between December 12 and December 19 2016. Using Yin’s 5 step data analytic procedure, the 6 themes that described the pattern between immigrant’s wellbeing and their financial literacy levels are social institutions, economic institutions, pressure impacting financial decisions, credit facility impacting financial decisions, emotions impacting financial decisions, and discount deals impacting financial decisions. The results from this qualitative study might trigger positive social change if immigrants to Canada develop their financial literacy levels and stay committed to making sensible financial decisions.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".