GOVERNMENT DIGITALIZATION: EVALUATING EFFECTIVENESS AND RISKS FROM PUBLIC PERSPECTIVE
Bibliographic record
Abstract
Digital transformation is one of Russia’s national goals and development objectives. Therefore, monitoring sociological studies aimed at collecting public perceptions of effectiveness and risks of government digitalization are highly relevant. The goal of this paper is to evaluate the effects of government digitalization from the public perspective. The subject of the study is the interaction between the state (public authorities and state institutions) and citizens with the use of digital technologies during the performance of publicly relevant government functions. The primary method of the study is a representative sociological public survey. The results presented in the paper contain an evaluation of the effectiveness of government digitalization from the citizens’ perspective, an analysis of the public readiness to engage in digital interaction with the public authorities as well as an evaluation of the risks associated with government digitalization, as perceived by the public. The paper demonstrates that a vast majority of Russians (88.2 percent) have engaged in digital interaction with the state over the past year. The study concludes that while overall evaluation of government digitalization effectiveness in terms of improving governance quality is positive, some areas are more problematic. For instance, less than a quarter of the respondents feels that the use of digital technology has improved the quality of education. Despite significant experience in digital interaction with the state, most respondents are not always ready to choose the digital channel for every issue. The choice of digital channel over other possible interaction means mostly depends on digital skills. The respondents evaluate the risks associated with government digitalization as high. Better educated respondents with higher digital skills are more likely to note that government digitalization risks are substantial, compared to other citizens. The novelty of the study is related to developing and implementing sociological instruments for measuring effectiveness and risks of government digitalization as perceived by the public. The paper recommends to account for the public evaluation of digital government effectiveness and risks while planning and implementing the government’s digital transformation initiatives at the federal and at the regional level.
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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 | 0.027 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".