Bibliographic record
Abstract
Problem Discussion Globalization and social media are factors that enhance the importance for countries to communicate their identity. The dynamics of competition has changed and in order to improve economic performance, attract tourism, trade and investment opportunities a strong country brand is needed. Project Objective This report aims to illustrate how country branding is done in China today, potential development during coming years and what is needed to succeed in creating a strong country brand in China. Furthermore, the report focuses on Sweden’s presence in China and Sweden’s country branding efforts. Methodology A qualitative approach for the research has been conducted with a cross-sectional approach. The information has been collected primarily through in-depth interviews and desktop research. Theoretical Framework The theoretical framework consists of the expertise of authorities within the field of country branding; Philip Kotler, Wally Olins and Simon Anholt, published research and publications as well as additional desktop research. Empirical Data 23 in-depths interviews have been conducted with, among others, government bodies and company representatives, covering 13 countries; Canada, Chile, Finland, France, India, Indonesia, Israel, Malaysia, South Africa, Sweden, Switzerland, the UK and the US. Conclusion China will soon become the largest economy in the world. At the same time the competition on today’s market has never been tougher. Countries are no longer platforms from which companies operate, they are brands, and are all fighting for China’s attention. To develop a successful branding strategy in China countries need to understand the level of government control, what the government is looking for, and the competition it experiences from the social media. A branding strategy has to be suitable on a regional and local level, not just at a national level. It demands an understanding of changing values and expectations of the Chinese people both in the sense of a rapid diverse economic development but also due to geographical and cultural differences across the nation.
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.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".