Place branding strategies through Winter Olympic games
Bibliographic record
Abstract
Purpose The purpose of this research is to examine how place branding strategies, undertaken by countries which belong to distinctive clusters, can manifest differently via the Winter Olympic Games.Methods In this paper, the experiences of Winter Olympics hosted by three countries (Russia, South Korea, China), are examined using Richelieu et al.’s framework, alongside clusters introduced in 2022. The study adopts a content analysis.Findings The framework is validated: the perceived importance of soft power and a country’s resources, differences in the measures and strategies used by the host countries, are confirmed.Practical implications Countries that set higher importance on sport diplomacy (Russia and China: “diplomacy branding”) are more assertive. In contrast, South Korea takes a more balanced approach, as it considers its action across a wider spectrum of areas (“balanced structure”).Research contribution The framework can explain the differences in the experiences of host countries, by also referring to the public choice theory. This paper illustrates how place branding strategies through sporting events, such as Winter Olympics, can differ between countries depending on the perceived role of public diplomacy, sport diplomacy, soft power and the economic resources invested.Originality/Value The article widens avenues for future research on major sporting events, through different clusters, by borrowing a holistic approach.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".