DIGITALIZATION OF THE ORGANIZATIONAL ASPECTS OF PUBLIC OVERSIGHT AND THE PRESSING CHALLENGES IT FACES
Bibliographic record
Abstract
This article examines the need for digitalization of public control and the organizational and legal basis for its implementation. The subject of the research is the issues of organizing public control through the use of digital technologies in the context of interaction between public administration and civil society. The relevance of the topic is determined by the need to strengthen trust between the state and society, ensure transparency, and ensure the active participation of citizens in management processes. The purpose of the study is to develop effective mechanisms of public control through the use of digital technologies, in particular, blockchain, artificial intelligence, and mass media. Within the framework of the research, methods of scientific and theoretical analysis, a systematic approach, and analysis of international experience were used. The possibilities and risks of implementing digital public control in the context of Uzbekistan were analyzed, and the experience of Canada, Sweden, and the European Union countries was considered. As a result, the necessity of forming digital public control, strengthening cooperation between the state and society, increasing the digital literacy of citizens, and strengthening the legal framework was substantiated. In the conclusion, the author notes that these results can be applied in the field of public administration, civil society institutions, increasing social activity, and digital services, and puts forward practical recommendations for improving the legal, organizational, and technological foundations of digital control.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.028 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".