INSTITUTIONAL ENVIRONMENT OF THE PORK MARKET: THE ROLE OF FORMAL AND INFORMAL MECHANISMS
Bibliographic record
Abstract
Introduction. The pork market in Ukraine is one of the key segments of the national agri-food system, providing a significant part of consumer demand for meat products, generating employment in rural areas and being a source of fiscal revenues. Its strategic importance is growing in conditions of full-scale war, global instability and aggravation of food security challenges. Methods. The complex of general scientific and special methods, which ensured a comprehensive study of the institutional environment of the Ukrainian pork market, were used during the study. Systemic and comparative analysis allowed us to investigate the relationships between formal and informal institutions and compare the features of institutional support in Ukraine and other countries. Content analysis of regulatory acts and expert surveys made it possible to assess the effectiveness of formal mechanisms and identify the role of informal practices, such as trust, reputational interaction, and cooperation. Results. The results of the study showed that the institutional environment of the pork market in Ukraine is in a state of imbalance: formal institutions remain ineffective, while informal practices partially compensate for these gaps, but at the same time undermine legal competition. It was established that strategic reform should combine the adaptation of formal mechanisms (certification, financing, tax incentives, cooperation) with partial legalization and integration of informal practices through digital tools of trust and transparency. As a result, it is the synergy of formal and informal institutions that can ensure fair competition, increased food security and sustainable development of the meat industry in Ukraine. Discussion. In the prospects of further research into the institutional environment of the pork market, the primary direction is an in-depth analysis of the interaction of formal and informal institutions at the regional level, taking into account the specifics of the development of the industry in different regions of Ukraine. At the same time, research into the effectiveness of institutional support for small and medium-sized producers using empirical methods (surveys, interviews, case studies), as well as analysis of coordination models between state bodies, professional associations and business, remain important. Promising directions also include comparing Ukrainian practices with international experience (EU, Canada, Brazil), studying the impact of digitalization on institutional processes and modelling the role of institutions in ensuring the stability of the pork market in the face of military and global challenges. Keywords: institutional environment, pork market, formal institutions, informal mechanisms, agricultural policy, regulatory capacity, vertical integration, competition, state support, agricultural sector.
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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 | 0.005 |
| 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 | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| 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".