DIGITAL MEDIATION TOOLS IN RESOLVING SOCIAL CONFLICTS WITHIN THE PUBLIC ADMINISTRATION SYSTEM
Bibliographic record
Abstract
This study examines the use of digital mediation tools to resolve social conflicts within the public administration system, emphasising their growing importance in the context of the global digital transformation of governance. The research focuses on the integration of online platforms, artificial intelligence technologies and digital communication formats into public governance mechanisms for resolving conflicts. The primary aim of the research is threefold: to assess the effectiveness of digital mediation tools; to determine the level of trust in these mechanisms; and to propose a methodological framework for their evaluation, with a particular focus on the Ukrainian context during wartime recovery and governance decentralisation. In order to achieve these objectives, the authors employed a comprehensive research methodology that includes comparative analysis, content analysis, sociological surveys, and mathematical modelling. The comparative analysis focused on international experiences from countries such as Estonia, Germany, Canada, Singapore, and Ukraine, with a view to identifying best practices in digital mediation implementation. A content analysis of digital platforms was conducted to assess functionality, interactivity, and usability. A sociological survey was conducted, with 200 respondents including public officials, local community members, and mediators. The aim of the survey was to capture perceptions regarding trust, accessibility, and barriers to participation. The development of three key indices was enabled by mathematical modelling: the Index of Digital Mediation Accessibility (IDM), the Index of Digital Mediation Effectiveness (IEM), and the Index of Stakeholder Satisfaction (ISM). Collectively, these indices form a Composite Digital Mediation Index (CEM), the purpose of which is to quantify overall effectiveness. The findings indicate that digital mediation is gaining traction in public administration, facilitating transparent dialogue, broader participation, and efficient conflict resolution processes. In Ukraine, the VzaemoDIA platform and other online consultation tools have become instrumental in fostering civic engagement, particularly in regions affected by conflict or remote communities. The Composite Index calculated in the study indicated an 75% effectiveness rate, with the highest performance recorded in the stakeholder satisfaction component (83%). These results indicate that Ukrainian society is prepared to adopt digital conflict resolution tools, although there is a necessity for consideration of digital inequality, digital literacy, and data security. The study concludes that, although digital mediation cannot replace traditional methods entirely, it is a vital addition to modern governance, particularly in times of crisis. To maximise impact, policy measures should prioritise integration with broader e-governance systems, as well as providing training for public officials and citizens, developing cybersecurity infrastructure, and legally regulating online mediation processes. This study makes a valuable contribution to academic discourse by proposing a replicable evaluation framework and offering insights into Ukraine's distinctive experience of managing digital conflicts during wartime.
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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 | 0.015 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 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".