Institutionalization of lobbying in Ukraine: theoretical and legal foundations and models of implementation
Bibliographic record
Abstract
The article examines the phenomenon of lobbying as one of the key civilized technologies of influence on the formation of public policy in democratic countries and analyzes the current state of its legal regulation in Ukraine. Lobbying in its modern sense appears as a legitimate instrument for representing and protecting the interests of various social groups, businesses, professional communities, and civil society institutions. In developed democracies such as the United States, Canada, and EU member states, lobbying activities are strictly regulated by special legislation that defines the status of lobbyists, procedures for registration, reporting mechanisms, and ensures transparency in interactions between the private sector and public authorities. Such regulatory models promote openness of the political process, reduce corruption risks, and ensure a balance of interests. Despite long-standing discussions on the need to legalize lobbying, Ukraine is still at the stage of developing normative approaches to its regulation. Key challenges include the absence of a clear legal definition of lobbying activities, the lack of regulation of the status of lobbyists, the opacity of private influence on decision-making processes, and high corruption risks. The article emphasizes that the shadow nature of lobbying practices undermines public trust in state institutions and complicates Ukraine’s integration into the European legal space, where transparency and accountability are fundamental principles of public governance. Special attention is paid to the comparative analysis of international models of lobbying regulation that may be implemented within Ukraine’s legal system. The article highlights the importance of adopting a special law on lobbying, establishing public registers of lobbyists, and introducing mechanisms of oversight and liability for violations of lobbying rules. It is argued that the legalization of lobbying in Ukraine can ensure transparency in the decision-making process, contribute to the development of democratic institutions, reduce corruption levels, and create civilized mechanisms for representing public and private interests within state policy formation.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".