NATION BRANDING AS A MECHANISM OF PUBLIC GOVERNANCE: INTERNATIONAL EXPERIENCE OF THE UNITED STATES AND OTHER COUNTRIES FOR UKRAINE’S IMAGE POLICY
Bibliographic record
Abstract
The article examines the international experience of nation branding as an instrument of public governance for Ukraine based on case studies from the United States, Canada, Japan, and South Korea. It analyzes institutional models of image-making, mechanisms of public diplomacy, and communication strategies aimed at enhancing a state’s reputation and global trust. The study emphasizes how image management has evolved from a cultural activity into a structural component of statecraft – a measurable function of governance that ensures soft power, legitimacy, and sustainable development. It further conceptualizes nation branding as a form of “reputation governance,” in which communication becomes an operational tool of policy performance, crisis response, and international positioning. The comparative approach allows the identification of universal principles of successful branding – long-term institutional continuity, strategic coherence, and social inclusivity – that enable states to convert symbolic capital into diplomatic and economic advantage.Special attention is paid to Ukraine’s image transformation before and after 2022, showing how crisis diplomacy and cultural resilience shaped the country’s new brand identity. The research highlights Ukraine’s transition from a peripheral post-Soviet perception to a central narrative of democratic leadership and civic bravery within the European political imagination. This transformation is viewed as part of a broader paradigm shift – from reactive image management to proactive trust governance – demonstrating how public communication, digital diplomacy, and cultural policy can reinforce national legitimacy during wartime and reconstruction.The article concludes that the institutionalization of image policy through strategic coordination and reputation analytics can strengthen Ukraine’s standing in global communications and integrate its national brand into the European and transatlantic discourse. It recommends the creation of a National Branding Office, the application of data-driven performance metrics (KPI and soft power indices), and the systematic inclusion of civil society in image governance processes. Ultimately, the research argues that for Ukraine, nation branding is not merely an element of external communication but a strategic infrastructure of resilience, credibility, and post-war recovery.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".