Determinants of Financial Literacy: Analysis of the Impact of Family and Socioeconomic Variables on Undergraduate Students in the Slovak Republic
Bibliographic record
Abstract
Technological progress and the development of electronic services make financial services one of the fastest-growing sectors. The role of the current education system is to ensure that all users of an ever-increasing variety of products and services understand them and are able to use them efficiently. However, in terms of gender, socioeconomic, and demographic factors, the existing system of financial literacy education exhibits considerable disparity. The main goal of this research was to identify which factors had the greatest impact on the level of financial literacy and to analyse the magnitude of that impact. The study involved 363 first-year undergraduate students at the University of Žilina, Slovakia, and consisted of two parts—a questionnaire and a test that evaluated the impact of five groups of factors on the level of financial literacy. The research results suggest that the student’s gender, father’s education, family’s financial background, and student’s part-time work experience were among the most important determinants of financial literacy. Identifying these factors can aid in the adjustment of financial literacy education to reduce identified inequalities.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".