Advocacy of Competition in the Mechanism of State Regulation of the Economy
Bibliographic record
Abstract
The multifaceted competitive policy promotes awareness of the importance of competition by society, ensures maximum transparency of state regulation, reduces the level of corruption and increases public confidence within activities of the competition authorities, helps developing self-regulation of economic entities. At the same time the mechanism of state regulation and self-regulation has its own instruments for improving the efficiency of advocating competition. Research of their peculiarities, instruments, role and interaction are important directions of modern scientific investigations and the purpose of this article.In this article a comparative method to study is usedfor common and distinctive features of advocating of competition in different countries and Ukraine. The results show that it is extremely important to create a system for advocating of competition in order to inform society, protect the attained level of competition in entrepreneurial activity, prevent or suspend, and then stop the abuses of monopoly position, the anti-competitive concerted actions of business entities, the anti-competitive actions of state authorities and unfair competition. The experience of economically developed countries convincingly suggests that such practices have a positive effect on the functioning of the competitive environment in which the interaction of economic agents takes place. The results show that there was a gap in implementation of economic policy in Ukraine and there is a gap between legally established norms on the implementation of competition policy and the practice of their application. To a large extent, this is due to the lack of well-developed strategy for economic development in Ukraine and, accordingly, the strategy for the development of competition policy.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".