Development of a Model for Assessing the Potential Impact of Blockchain Technologies on Economic Growth Dynamics in Financial Markets
Bibliographic record
Abstract
Over the past years, blockchain technologies have become one of the outstanding innovations in the financial sector of the economy, enhancing and facilitating transaction procedures in many spheres. Moreover, these technologies are of great significance concerning the financial market, including regulators. However, it may well involve a certain level of uncertainty of the generated effects both for themselves and for the national economy. It is worth mentioning that quite many works devoted to the problems posed focus on qualitative assessments and conclusions, focusing either on the study of the technological component of the technologies or on the regulatory and legal ones. This study aims to reinforce the positions of formalized approaches to the study of the scientific and practical problems posed. The paper proposes an algorithm for studying the influence of blockchain technologies on the GDP dynamics through the prism of the transformation of key functioning parameters describing the financial and real sectors of the economy. A cointegration model has been built that allows one to determine the main effects and the potential impact of possible transformations (as a result of the penetration of blockchain technologies into the system of economic relations) of individual functional areas in the financial sector of the economy on GDP dynamics. The obtained estimates of the sensitivity of economic dynamics to the considered adjustments of the financial market demonstrated the potential for economic expansion, provided the possible integration of blockchain technologies in the business environment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".