Integral estimation of the competitiveness level of the western Ukrainian gas distribution companies
Bibliographic record
Abstract
Innovative policy and regional management leverages should create the foundations for the “impulse” to form the Ukrainian market of natural gas and its distribution. Efficient and desirable economic reforms in the activity of the gas distribution companies of the Western region of Ukraine will contribute to increasing their competitiveness level by implementing innovative activities. The paper aims to make an integral estimation of the competitive positions of the gas distribution companies that function on the Western Ukrainian market of natural gas distribution among consumers and develop practical recommendations directed at forming and implementing the innovative policy of improving their competitiveness. To achieve the aim, the authors have developed the methodology of calculating the competitiveness level for the gas distribution companies that function on a certain natural gas distribution market of any scale. Based on the results of the conducted research, the paper shows that the competitiveness condition of most gas distribution companies of the Western region of Ukraine is either of critical – І or satisfactory – ІІ levels. Meanwhile, it is worth mentioning that only AT “Chernivtsihaz” is characterized by the highest rate of competitiveness integral estimation among all the companies operating on the market of the natural gas distribution among consumers in the Western region of Ukraine. Therefore, it was identified as level ІІІ – “decent” level, which is the average competitive indicator.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".