Strategic Cooperation of Ukrainian Industrial Enterprises to Create Competitive Advantages in the World Market
Bibliographic record
Abstract
Competitive advantages in the market can be accumulated both with the use of the mechanism of cooperation, and as a result of coordination in the interregional sphere. The relevance of the study is determined primarily by the fact that cooperation between organisations allows to increase competitiveness in the foreign market. However, this gives rise to a contradiction that determines that cooperation between companies is possible only if the conglomerate or certain entities own controlling shares. With that, companies should not only constitute holding structures, but primarily be co-operators in the production cycle. The novelty of the study is determined by the fact that strategic cooperation is proposed to be considered not only as a set of practical actions on the part of the state or regulatory structures, but also of consulting bodies. It is proposed to use the mechanisms of strategic cooperation based on mutual conditionality of interests and security of budgetary mechanisms that allow for practical activities. The authors also admit the possibility of the use of public-private partnership mechanisms. The practical significance of the study is determined by the fact that each of the participants in the organisation of strategic management of enterprises can use not only strategic, but financial and systemic interaction mechanisms to form.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".