Digital Divide of Regions: Possible Growth Points for Their Digital Maturity
Bibliographic record
Abstract
The purpose of the study is to research the process of digitalization of the regional economy and society in order to identify the root causes for discrepancy in their progress and determine the main directions for improving regional management systems to reduce their digital inequality.This article analyzes the digital development of regions in Russia, based on statistical data from 2013 to 2020.The results show a significant gap between the leading and the underdeveloped regions.The study identifies the root causes of the imbalance and backwardness in digitalization, and provides insights for the further digital transformation of regions in the implementation of the Digital Economy National Program.The proposed methodology to assess the digital competitiveness of regions provides for an analysis of the key areas of digital transformation directly related to the digitalization of the public services sector, the economy and the social sphere.It enables to consider the innovative potential in the regional context, track the digital transformation of organizations and their involvement in digital ecosystems, and identify changes in households in terms of connecting to ICT and using personal computers, based on their digital literacy and competencies.The article provides valuable insights, which can be useful for managers and members of the scientific community involved in evaluating the effectiveness of developing the digital potential of regions and in promoting digital transformation in Russia.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".