THE ROLE OF INFORMATION AND COMMUNICATION TECHNOLOGIES IN RURAL DEVELOPMENT
Bibliographic record
Abstract
The article aims is to analyze the internet utilization patterns of rural households in selected European Union countries, particularly focusing on Poland, and to assess the specificity and level of differentiation in this area between countries. The study divides European Union (EU) countries into clusters based on the share of rural residents’ using the internet from 2004 to 2022. Employing Ward’s method and k-means clustering with the squared Euclidean distance measure, cluster analysis is used for country grouping. Internet functionalities are analyzed to understand consumer behaviors. The study shows that European countries can be categorized into four distinct groups according to the percentage of rural inhabitants who had access to the internet. Internet functionalities revealed variations in accessing information, scheduling medical appointments, social networking, online courses, and political engagement across these clusters. It was found that digital inequality among rural inhabitants in the EU persists, with varying levels of internet usage and utilization of internet functionalities. The convergence hypothesis suggests that less developed areas experience faster growth in internet usage, potentially reducing disparities. Policies promoting digital inclusion and advanced digital skills training are essential to bridge the digital divide in rural areas. In conclusion, access to internet functionalities, especially in healthcare and education, remains a challenge that requires attention. The study emphasizes the importance of considering cultural and socio-economic contexts in understanding digital inequality. This research sheds light on the digital divide in rural EU regions and highlights the need for targeted interventions to enhance digital inclusion and improve the quality of life for rural residents.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".