Combating shadow processes in the agricultural sector: the role of state regulation.
Bibliographic record
Abstract
The purpose of the article is to investigate shadow economic processes in Ukraine's agricultural sector and to define the role of state regulation in counteracting their expansion. The study applies a comprehensive methodological approach, including system analysis, comparative evaluation of foreign experience (notably in Poland, Germany, Brazil, and Canada), structural-logical modeling, and elements of expert judgment. The results indicate that despite the implementation of several government programs aimed at economic reform and de-shadowing, the agricultural sector remains highly vulnerable to informal practices. Key contributing factors include fragmented production, insufficient land accounting, unequal access to resources, and limited institutional capacity at the local level. The scientific novelty of the study lies in the development of a structural-logical scheme for assessing the effectiveness of state regulation of shadow processes, along with the identification of improvement strategies based on sectoral and regional specificity. The practical significance of the research is determined by its applicability for shaping more adaptive and regionally sensitive public policies in the agricultural domain, particularly in the post-war recovery context. The proposed model and recommendations can contribute to enhancing transparency, fiscal discipline, and competitive equality among agricultural producers. Future research prospects include the development of measurable indicators for the level of shadow activity in the agri-food sector and designing incentive-based mechanisms for voluntary legalization of economic operations.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".