Economic and Legal Policy of the State in the Field of Digital Economy
Bibliographic record
Abstract
Digitalisation of the economic sphere of Ukraine is one of the priority areas of social development, which should be reflected in the economic and legal policy of the state. Therewith, a considerable gap exists in the statutory consolidation of the fundamental principles of such a policy, in particular the lack of relevant provisions on the digital transformation of the economy in the Commercial Code of Ukraine. Despite the fact that the state has adopted some acts on the development of the digital economy, there are significant gaps in the legislation, especially with regard to the role of digitalisation in the economic and legal policy of the state. The purpose of the study is a theoretical analysis of the general principles of such a policy in the modern period. As a result, the impact on the economic and legal policy of the state of the process of digitalisation of the economic sphere of the country is determined. The necessity of co-regulation is substantiated, which constitutes a combination of state regulation with self-regulation by participants of digitally-targeted markets. The role of digital resources (on the example of public registers) and electronic financial services is analysed. It is established that the modern legal support of the digital economy needs to be reformed, first of all, by supplementing the Commercial Code of Ukraine with provisions on digitalisation, including the relevant direction of economic and legal policy of the state. The results of the study have a scientific novelty, as they fill the gap in the coverage of the role of the state in the process of digitalisation of the economy.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".