Complex approach to assessment of competitiveness of power-generating companies of developing economies
Bibliographic record
Abstract
The present-day trends in the economic development are characterized by both the processes of restructuring initiating investment activity and the mounting competitive pressure.These special characteristics clearly manifest themselves in the developing economies featuring low level of development of infrastructure -in the power sector in particular, and this gives rise to the development of specific forms of competition in the power-generating sphere.Finding solutions to the problems of development of energy infrastructure will be instrumental in strengthening the competitive position of the developing countries on the world market and reducing the threat of takeover.This paper presents a complex approach to assessment of competitiveness of power-generating companies in developing countries.Such an approach offers an opportunity to assess the attractiveness of current levels of investment of a company and its long-term sustainability through application of modern analytical tools.The practical aspects of the authors' methodological approach to the assessment of competitiveness are discussed using a Russian power-generating company as an example.The proposed ideas based on revealing the most risk-bearing hazards, those of latent nature including, may serve as a methodological basis for the development of risk management programmes in the power-generating sphere, to the benefit of realization of investment projects as well.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".