MECHANISMS OF COMMUNICATION BETWEEN PUBLIC AUTHORITY BODIES AND THE PUBLIC
Bibliographic record
Abstract
The article explores modern mechanisms of communication between publicauthorities and the public in the context of digital transformation. It examines the use ofsocial networks, e-government, artificial intelligence, data analytics, and interactivecitizen participation platforms as effective tools for interaction between government andsociety. The role of digital technologies in ensuring openness, transparency, and feedbackin public administration processes is highlighted. The study emphasizes the importanceof enhancing cybersecurity, protecting personal data, and improving the digitalcompetence of civil servants. References1. Reznikova, O. O. (2022). National resilience in a changing security environment:monograph. Kyiv: NISS.2. Konyk, D. (2020). Community trust: Crisis communications of local selfgovernment bodies: A practical guide. Federation of Canadian Municipalities /International Technical Assistance Project «Partnership for Local EconomicDevelopment and Democratic Governance (PLEDDG)».3. Zahorskyi, V. S., & Petrovskiy, P. M. (Eds.). (2021). Public administration inUkraine: Problems and prospects for development: monograph. Lviv: LRIDUNADU.4. Dziana, H. O., & Dzianyi, R. B. (2021). Tools for ensuring the effectiveness ofcommunicative activities of public organizations. Democratic Governance:Scientific Bulletin, 1(27). Lviv: LRIDU NADU.5. Husiev, A. I. (Ed.). (2020). Communicative technologies of the informationsociety: A monograph [A. I. Husiev, N. O. Dovhan, O. V. Ivachevska,N. S. Malieieva, I. V. Petrenko]. National Academy of Educational Sciences ofUkraine, Institute of Social and Political Psychology. Kropyvnytskyi: Imex-LTD.
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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 | 0.016 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".