DIGITAL MEDIATION TOOLS IN RESOLVING SOCIAL CONFLICTS WITHIN THE PUBLIC ADMINISTRATION SYSTEM
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,015 | 0,034 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,004 | 0,003 |
| Études des sciences et des technologies | 0,004 | 0,006 |
| Communication savante | 0,009 | 0,013 |
| Science ouverte | 0,002 | 0,012 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».