MECHANISMS OF COMMUNICATION BETWEEN PUBLIC AUTHORITY BODIES AND THE PUBLIC
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| 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 it