Small and medium-sized agribusiness: modern realities, problems and ways of solution
Bibliographic record
Abstract
The goal is to identify priorities for the creation of a service sector in agriculture, consumer services for the population, production of professional and handicraft products, the development of health-related facilities, promotion of national traditions, and creation of opportunities for wider dissemination of these industries. In order to accelerate the development of small and medium-sized businesses in the country's agricultural sector, provide employment to rural residents based on strengthening their socio-economic status, improve life in the region, and eliminate financial deficits, it is necessary to increase the level of provision of agricultural producers with material and technical resources. Methods – comparative analysis, generalization, systematic approach, SWOT analysis. Results – the priority directions for the development of small and medium-sized agricultural businesses, public support, imbalances in prices for agricultural products and industrial goods, expansion of market infrastructure, timely execution of mutual payments between farms, the need to limit the growth of costs and large volumes of imports of food products, measures to increase entrepreneurial activity and competitiveness in the regions are shown. A SWOT analysis of the activities of small and medium-sized business entities was carried out. Based on the results of the study, ways to solve the identified problems were proposed. Summarizing the results obtained, conclusions are formulated within the framework of this study. The degree of development of small and medium-sized households affects the saturation of the market with goods, expansion of competition, regulation of employment, and positive social transformations in rural areas. Small agricultural enterprises have untapped entrepreneurial potential, which can be used taking into account the introduction of digital technologies, new equipment, rational organization of labor and management. The experience of the USA, Canada, and France should be widely applied and adapted to domestic realities.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".