Infrastructural and Social Aspects of ICT Dissemination in Rural Areas in Ukraine in Juxtaposition with Other Post-Transition Countries—State of Play and Prospects for Rural Development
Bibliographic record
Abstract
The objective of this study is to identify the current state of, and the prospects for, information and communication technologies (ICT) dissemination in rural areas in Ukraine in juxtaposition with other post-transition countries. The spread of ICT is discussed within the frame of economic, infrastructural, and social factors affecting rural areas in Ukraine since the post-communist transition period. Information and communication technologies may support the socio-economic development of peripheral areas in many ways—including rural ones. Dissemination of ICT contributes to the emergence of sources of income, equalizes education opportunities, and increases the attractiveness of rural areas. However, the rural—urban divide in the countries of Central and Eastern Europe and other former USSR countries is still remarkable and, as a type of structural inequality, should be better recognized. The source material is based on secondary data, which consists of selected literature on the subject of rural development in Central and Eastern European Countries, strategic documents, available reports and studies of international institutions, research from agencies, state documents and statistics, and research conducted by international and domestic NGOs. In reference to the paper’s objective, the method of content analysis was employed. Dissemination of ICT in rural areas in Ukraine is influenced by two groups of factors. The infrastructural divide concerning Internet access between rural and urban populations in Ukraine has been diminishing, but the issue of structural exclusion due to place of residence has still not been solved. As far as the social aspects of ICT dissemination in rural areas in Ukraine are concerned, the level of digital literacy among rural dwellers is significantly lower in comparison to urban residents. Rural areas are more exposed to the consequences of various aspects of digital exclusion.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".