Bibliographic record
Abstract
Financial services and their availability are integral elements of modern society’s well-being. The main causes of economic and financial exclusion, such as: illegal work, access to Internet financial services (lack of computer literacy, communication limitations, difficulties in accessing a branch of a financial institution), over-indebtedness. The scale of over- indebtedness in Lithuania equals to quarter of the annual budget of Lithuania. Every year, the number of cases handled by bailiffs grows along with the amount of money owed. Residential and household schools are important objects of research. In Lithuania, the problem of financial inclusion and exclusion is not actively analysed. Hope that this study will contribute to the development of the topic of over-indebtedness and financial exclusion among researchers. The purpose of this study is to analyse total household debt as a threat to financial exclusion. The implementation of the goal by setting the task: to assess the total debts of Lithuanian and European households in the context of socioeconomic indicators of financial exclusion. To achieve the research objective, used secondary analysis of the 2021 macro data. The data used are at the level of European countries. Used data from the Eurostat, European Central Bank database. The countries selected for analysis are Denmark, Norway, Switzerland, the Netherlands, Sweden, Finland, Belgium, France, Germany, Spain, Austria, Greece, Slovakia, Italy, Poland, Lithuania, Hungary, Latvia, Estonia. The selection of countries was influenced by the availability of the necessary data. The Pearson correlation coefficient is used in order to assess the relationship between the general debts of households and indicators of financial exclusion and poverty risk. Hierarchical cluster analysis is used in order to assess the situation of general debts of households in the context of European countries. In relation to income, Lithuanian general household schools are among the lowest among the analysed European countries. The results of the study show that a higher level of indebtedness of households at the national level does not lead to a higher risk of financial exclusion. Higher total household debt indicates the country’s level of development, a lower poverty risk indicator, and a higher number of bank account holders.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".