Conceptual models of state support for agriculture: From direct producer support to financing general agricultural services
Bibliographic record
Abstract
Since the concept of state support for agriculture in Ukraine is not perfect and requires improvement, given the limited financial capabilities of the country during a full-scale invasion and post-war recovery, the aim of the article was to find an optimal model of state support based on the experience of countries with a highly developed agricultural sector. The features of conceptual models of state support were considered through indicators of structural and dynamic analysis and assessments of the effectiveness of budget support in the EU, Canada, New Zealand, the USA, and Ukraine. The premise of the study was an analysis of the overall imbalance level of state support directions for agriculture, according to the results of which none of the countries had a balanced approach to regulating the agricultural sector. It was established that the reason for the highest levels of imbalance in the EU was the undisputed dominance of producer support and, in the USA – consumer support, simultaneously with a low priority of financing for agricultural knowledge and innovations in both countries. A hybrid model of state support turned out to be a feature for Ukraine since dissimilar directions prevailed during different periods of turbulence. Given the assessment of the effectiveness, Ukraine should use the Canadian model in the short term, which, although similar to the EU, supports farmers on a much smaller scale and has a relatively upper focus on general services for the agriculture, in particular inspections and controls and knowledge and innovations. It was established that in the long term, the priority is to adapt the New Zealand approach with the absolute dominance of investments in infrastructure, research and technological development, which ensures maximum economic effect and competitiveness of the industry. The findings of the study can be used by the Ukrainian authorities, in particular the Ministry of Agrarian Policy and Food, to improve state support programmes for agriculture by adapting effective financing models based on international experience and taking into account the economic and resource realities of the country
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".