Farm Size and Technology Implementation: A Comparison between Canada and Ukraine
Bibliographic record
Abstract
Many factors play a vital role in the development of agriculture, which include the technology of production, the size of farms in the country and the national policy (including trade policy) in relation to producers of these products. Therefore, the analysis of the above-mentioned factors in Ukraine stays relevant. The purpose of this study was to investigate the situation in the agricultural sector of both countries to form methods of further development of the sector in Ukraine based on the Canadian practices. The leading research method is analysis, thanks to which the agricultural sector was studied. In addition, the comparison method was used in the study of agriculture in Ukraine and Canada. Canada uses the latest methods of growing and tending produce, while in Ukraine there is still manual labour in some enterprises. It was proved that the main reason for this difference in development is the limited ability of Ukrainian companies to attract investment or use credit. The authors concluded that there are fundamental differences in agricultural development in Ukraine and Canada, the reasons for which are explained not only by different geographical, but also by institutional and historical conditions. Meanwhile, the level of agricultural development in Canada is much higher than in Ukraine, showing the need to borrow some principles of the sector. The main ones among them include active attraction of investments, emphasis on technology development, minimal state interference in the sector and others. A more detailed consideration of finding new opportunities to attract investment in the agricultural sector of Ukraine will remain relevant in the future. The article can be useful for studying the specific features of economic development of the agriculture in Canada and Ukraine; for formation of national policy in this sector; for entrepreneurs to make their investment decisions
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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 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".