DIGITAL TRANSFORMATION OF THE TRANSPORT SYSTEM IN THE REGIONS OF UKRAINE UNDER THE CONDITIONS OF MARTIAL LAW
Bibliographic record
Abstract
The article is devoted to the study of the features and directions of digital transformation at the regional level in Ukraine. The purpose of the work is to study the concept of digital transformation; assessment of digital technologies in transport; analysis of Smart-city cases; development of measures for the digital transformation of the transport system of Ukraine at the regional level. In the process of research, the authors used dialectical and abstract-logical methods, which revealed the nature of digital transformation and identified its principles. The application of the logical method made it possible to enrich the scientific characteristics of the concept of "digital transformation". On the basis of systemic and synergistic approaches, directions for the implementation of digital technologies in transport were evaluated. Methods of analysis and synthesis were also applied to research the international practice of digitalization of the transport system at the regional level, as well as to develop proposals for the digital transformation of the transport sector in the regions of Ukraine. The concept of digital transformation in work is defined as a process based on a moving from traditional models, involves qualitative changes in business processes or ways of conducting economic activity, as a result of which digital technologies are implemented, which leads to significant socio-economic effects. The work noted that transport and logistics are the industries in which the economic effect of digital transformation is most tangible. The transport system is most receptive to the introduction of such digital technologies as: Internet of Things, unmanned and mobile technologies, identification technologies, blockchain, big data, paperless technologies, drones, robotic systems, artificial intelligence and neural networks. The cases of digital transformation of regions (EU and Canada) and the Smart City project, an integral component of which is transport, were analyzed in order to determine the possibilities of their implementation in Ukraine under martial law. A number of recommendations have been developed to ensure the digitization of the transport system of the regions of Ukraine and the directions for its implementation have been determined.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".