Competitive Potential Branding Model of Subjects of Agro-food Economy Sector Ukraine
Bibliographic record
Abstract
The article examines the special conditions for the development of the competitive potential branding model of subjects of agro-food sector of economy of Ukraine. It is proven that the branding model in the system of subjects competitive potential of the agri-food sector of the economy ensures the value and effectiveness of the agricultural raw materials supply chain in the market with a low degree of processing, and also regulates the interindustry interaction of its participants. It is substantiated that the methodological basis of the competitive potential branding model of subjects of the agro-industrial sector of the economy is the calculation of the additional value of products in the supply chain of agricultural raw materials on the market with a low degree of processing, by combining global and national "Input-Output" indicators of a certain country, in the flow of bilateral trade The method of evaluating the competitive positioning of subjects of the agro-food sector of the economy on the market is presented. A collection of brand evaluation methods is grouped according to the factor indicator of the development of competitive branding of agro-food products. The economic activity of the dairy industry and the dairy production structure in the priority regions of Ukraine were analyzed. World and Ukrainian leaders of dairy companies on the market have been identified. Moreover, it has been proven that the competitive branding of subjects of the agro-industrial sector of the economy of Ukraine in the integration interaction with the EU countries contributes to the acceleration of investment activities for the modernization of the dairy production technology and their quality. An assessment of market attractiveness and competitive potential of the leaders of branded dairy products in Ukraine was carried out. The "McKinsey" matrix was built and the positions of dairy product leaders on the market were determined. Proposed measures to increase the competitive potential of dairy companies of the agro-food sector of the Ukrainian economy according to the composite scoring index Market Score, which determines the market opportunities and branding capabilities of the studied entities on the market. The difficulties faced by dairy companies of the agro-food sector of the economy of Ukraine during the war and the ways to solve them are analyzed as well.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".