The Role of Digital Technologies in the Transformation of Regional Models of Households’ Financial Behavior in the Conditions of the National Innovative Economy Development
Bibliographic record
Abstract
Households play one of the key roles in the development of the financial system of any country and the national economy in general. It is the understanding of the behavior of these economic entities in the market of financial services that makes it possible to predict the development of such a market, to understand the mechanisms of the emergence of dissipative processes in the interaction of households and financial institutions, which can form crisis phenomena in the development of such a system and restrain the innovative development of the national economy. This determines the importance and relevance of further research in this direction. Within the article, the impact of modern digital technologies on the development of financial services, in particular financial behavior of households, in the conditions of the formation and active development of the innovative economy is considered. Significant attention is paid to the specification of the methodology for determining the impact of the digitization index and the index of the model transformation of financial behavior of households. It is established that the outlined models are specific and different between various regions in any country. That is why the above method of calculation was used on the example of Ukraine. As a result, information was obtained on the digitalization index, and the transformation index of the financial behavior model of households in twenty-four regions of Ukraine. Based on the use of econometric analysis, algebraic equations of the dependence of the transformation index of the model of financial behavior of households on the digital technologies development in each of the outlined regions were determined.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".