Innovative and investment direction of farming enterprise development
Bibliographic record
Abstract
The aim of the research is to examine the innovative constituent of farming enterprises development, which is based on determination of the impact of elements of the resource component of production potential on the profit of farming enterprises. While assessing efficiency of management of the innovative development of enterprises, it is proposed to consider the impact of resource and financial factors (Grynko & Gviniashvili, 2017). It is also worth noting that the Canadian project GÇ£Development of dairy business in UkraineGÇ¥ is intended to eliminate the difficulties faced by small and medium-size milk producers. The authors of the work suggest that, under conditions of such common tendency, farming enterprises are not ready to take any radical measures concerning reconstruction and reorganization of their production because the attracted investments are primarily used for completion of the earlier started projects. To analyse the impact of cost elements of the production factors, which create the base of production potential, it is proposed to make grouping of farming enterprises of Ukraine by the level of land use in 2018. The analysis of the model (8.3) demonstrates that it provides explanation of 87% [almost similar to the model (8.2)] of the relation between the dependent and independent variables. It is considered that co-operation of farmers with local power authorities, in such context, should probably make a positive impact on the external environment of the farming enterprise, i.e., the community.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".