Agriculture Digitalisation as an Economic Growth Indicator (A Comparison of Private Farms in Ukraine and Germany)
Bibliographic record
Abstract
The article topic relevance is caused by the exclusive place of the agricultural sector in the Ukrainian economy, so its development will have a significant impact on the agro-industrial complex productivity and on efficiency of the state economy as a whole.The effectiveness of digital technology is confirmed by the example of developed countries, including Germany, the indicators of enterprises which in this article are the basis for comparison with Ukrainian subjects of state management.The article aim is to determine the necessity and substantiate methodological recommendations on implementation of the rural economy digitalisation as an indicator of economic growth in the agrarian economy sector based on the comparison of private farms of Ukraine and Germany.In the research, the general scientific methods were used, including the method of analysis, synthesis, and formalisation; method of comparative analysis; SWOT-analysis; PEST-analysis; graphical and statistical analysis.The research and analysis have allowed proving the necessity and grounds of theoretical and methodological recommendations on the active implementation of digitalisation in the industry.It was found, that for the efficient and safe process of digitalisation, it is necessary to improve the legislative basis for its support, to provide state support for the implementation of actions on digitalisation; improvement of access to information for Ukrainian farmers; creation of conditions for constructive dialogue with foreign scientists; stimulation of investment in science, technology; training of scientists and assessment and minimisation of risks that may be associated with implementation of digitalisation activities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".