Features of the Formation and Transformation of Household Credit Behavior Under Macroeconomic Instability
Bibliographic record
Abstract
Within the article, the formation and transformation of credit behavior of households in the conditions of macroeconomic instability is examined. The research was conducted on the basis of the analysis of the features of economic development and the development of lending to households in Ukraine in 2006-2021. At the same time, the justification of theoretical features of the change in the specified type of behavior was carried out. First of all, in the article, economic and political conditions in which economy of Ukraine developed during the outlined period are studied. Appropriate statistical indicators were used for this purpose. Also, in the article, taking into account the indicators of macroeconomic dynamics, a thorough study of the field of lending to households was carried out. This made it possible to describe the peculiarities of the change in the state of such crediting in different conditions of the country's economic development. A detailed analysis of individual parameters of bank lending to households made it possible to describe the formed model of credit behavior of these economic entities and conduct a statistical analysis of the impact of individual economic parameters on changes in the volume of such loans, establish the strength of their influence and importance for ensuring stable functioning of the lending sphere of these economic entities.
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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 | 0.000 | 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 | 0.001 |
| 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".