A Study on the Construction of Beijing City Image in the Mainstream Media of English-Speaking Countries
Bibliographic record
Abstract
Rooted in China's strategic needs to enhance its international communication capacity and cultural soft power, this study focuses on the construction logic of Beijing's city image in mainstream media of English-speaking countries. Adopting a combined research method of content analysis and discourse analysis, it constructs a corpus using Beijing-related reports (2020-2024) from five mainstream newspapers—The New York Times (U.S.), The Guardian (U.K.), The Globe and Mail (Canada), The Australian (Australia), and The New Zealand Herald (New Zealand), to systematically explore the characteristics of how Beijing's city image is constructed in these media outlets. The Three features are: (1) Media exposure demonstrates the characteristics of "generally low coverage, heterogeneity across countries, and event-driven dynamics"; (2) There is a structural imbalance in the mapping of Beijing's city image; (3) Significant national differences exist in theme selection. Essentially, such differences are the media reflection of the five countries' identity perceptions of China. Based on these findings, an optimization path featuring "differentiated and targeted communication+ multi-dimensional narrative upgrading + dynamic adjustment mechanism" should be adopted, so as to provide strategic support for Beijing to break free from the "othering" narrative of Western media and construct a three-dimensional and comprehensive international image.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".