Improving Efficiency of Asset Management in the Context of Ensuring Competitiveness of Mechanical Engineering Enterprises in Developing Countries
Bibliographic record
Abstract
The research paper deals with the formation of new scientific solutions regarding increasing the efficiency of asset management in the context of ensuring the competitiveness of mechanical engineering enterprises in developing countries. The study noted the disparity in the development of economically developed and developing countries. It is revealed that one of the main aspects of such disparity is the competitiveness of both the economies of the countries as a whole, and of individual industries and enterprises. At the same time, the importance of ensuring the competitiveness of mechanical engineering enterprises in developing countries was noted, taking into account their potential and opportunities for stimulating GDP and employment growth in these countries. The relationship between the competitiveness of mechanical engineering enterprises and their asset management is established. The main problems of asset management at the mechanical engineering enterprises in the developing countries are localized, and the ways of their elimination are proposed taking into account the division of enterprises into those operating as part of the transnational and foreign corporations, large enterprises with national capital, medium and small enterprises with national capital. A considerable range of problems regarding the asset management in small and medium enterprises was noted. Directions of further scientific researchers are suggested.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".