A Formalized Assessment of the Scenario Development of the National Economy in the Context of the Penetration of Blockchain Technologies Into the Financial Sector of Transactions
Bibliographic record
Abstract
In the coming years, blockchain technologies may become one of the breakthrough innovations in the financial sector of the economy, optimizing and simplifying transaction operations in a number of areas, and reducing their cost. In this regard, representatives of the financial industry should understand the possible consequences caused by the integration of the technologies under consideration in business processes. It is important to understand that the blockchain technologies have a very significant potential for transforming the established algorithms for the interaction of financial market participants, and be aware where are the boundaries of these changes, what new opportunities are presented by blockchain technologies and. Furthermore, finally, what are the expected consequences for the development of the financial sector itself and the other sectors of the national economic system associated with it. It should be noted that studies on blockchain technologies are very fragmented and, as a rule, have an insufficient level of both theoretical and empirical study. It seems relevant at the present stage of the development of economic science to conduct a systematic study aimed at finding and substantiating the areas of economic activity that are most susceptible to penetration of blockchain technologies. This involves a further justification of the possible consequences and using methods that are not so much qualitative but of quantitative analysis. In this regard, the process of resolving the issues posed in order to minimize risks for financial and other organizations in the face of the opportunities and threats to come under the pressure of the integration of blockchain platforms into the business environment becomes a vital and urgent task. This research is devoted to the solution of the questions posed. Without claiming to be complete and perfect concerning the proposed mechanisms to solve the tasks, the work, in fact, is an invitation to the scientific community to develop further a methodology for studying the influence of blockchain technologies on the dynamics and parameters of the formation of economic growth rates.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".