Planning of Resource Support for the Management System of the Process of Increasing the Level of Competitiveness in the Environment of the Functioning of the Socio-Economic System
Bibliographic record
Abstract
The main purpose of the article is to determine the best planning option for resource support of the management system for the process of increasing the level of competitiveness in the environment of the functioning of the socio-economic system. The relevance of the research topic is added by the fact that the ecology industry is extremely difficult and constantly needs innovation. The activity of an ecological socio-economic system depends on its competitive advantages, level of competitiveness, and resource support. The basic methods that form our methodology are the method of system analysis and pairwise comparison. Based on the results of the study, a structuring of all possible options for resource support for increasing the level of competitiveness of an ecological socio-economic system was formed on the example of Ukrainian ecological enterprises PJSC "Odessa Ecological Enterprise". So, for the ecological socio-economic system selected as an example, a choice of the most rational of the possible options was presented, resources for planning the management system in the process of increasing its competitiveness. The study has a number of limitations and they are related to the impossibility of applying the proposed approach to a larger number of ecological socio-economic systems. Further research should be aimed at expanding the practical aspect and covering more and more socio-economic systems of this sector.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".