INTERNATIONAL COMPETITIVENESS OF UKRAINE IN THE FIELD OF RAIL TRANSPORT
Bibliographic record
Abstract
The purpose of this article is to determine the competitive position of Ukraine in the field of railway transportation in comparison with Poland (a neighbouring country), Switzerland (a benchmark in the field of railway transportation) and Canada (which professes a completely different “American” principle of operating railway networks, where freight transportation is a priority). Methods. In the process of working on the article, the following research methods were used: analysis and synthesis in the selection of indicators and their grouping (production and financial, scientific and technical development, personnel characteristics) depending on the direction they characterise; methods of economic analysis – multidimensional comparative analysis, index analysis, Delphi method in determining the weight of indicators selected for analysis, rating to determine the final positions of each of the countries selected for analysis. Results. Among the main results of the study is the determination of Ukraine’s competitive position, primarily in comparison with Poland as a neighbouring country. The next most important is the group of indicators that put Ukraine behind the Swiss railways’ benchmarks, in particular, the quality of infrastructure and electrification. The scientific novelty of the results obtained is an attempt to assess the competitive positions of railway transport, which seemingly cannot compete with each other, since each of them operates in a separate territory (in most cases within the same country) and their interests hardly overlap. Currently, this is not entirely true, since, after the large-scale invasion, due to the blockade of Ukrainian seaports, a significant part of export and import commodity flows has moved to rail transport, and by analysing the situation in each individual transit country, it is possible to choose the direction that will be most acceptable for the long-distance transportation of goods to “third” countries. The practical significance of the results obtained can be viewed from at least two perspectives. The first is the indicators that have caused the Ukrainian railway to lag behind the benchmark state (Switzerland) and the work to improve the situation in each of the areas. The second side is the possibility of conducting a similar assessment of the situation in rail transport among all of Ukraine’s neighbouring countries and identifying the highest priority areas for sending most of the export and import cargo to “third” countries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".