Global Solidarity or Individual Rights?—A Comparative Critical Discourse Analysis of China’s National Image in China Daily and CNN’s Coverage of the Beijing Winter Olympics
Bibliographic record
Abstract
This study examines how China’s national image was constructed in media coverage of the 2022 Beijing Winter Olympics through a comparative critical discourse analysis of reporting by China Daily and CNN. Employing Fairclough’s three-dimensional model and corpus linguistics techniques, we analyzed a corpus of 60 China Daily articles (31,759 tokens) and 33 CNN articles (31,934 tokens) published during the Games (February 4–20, 2022). Keywords analysis revealed striking differences in reporting focus: China Daily emphasized development, cooperation, and international harmony using predominantly positive language, while CNN concentrated on human rights issues, LGBTQ+ representation, and political controversies through more critical framing. These divergent portrayals reflect underlying ideological orientations and socio-cultural contexts. China Daily adopted a macro-level perspective highlighting collective achievement and global solidarity, consistent with China’s communal values and historical continuity. Conversely, CNN employed a micro-level approach focusing on individual rights and minority concerns, reflecting America’s pluralistic cultural identity. Our findings demonstrate that national images are not static but dynamically constructed through ideologically-informed discourse, with media narratives serving strategic objectives even in contexts ostensibly dedicated to international cooperation. This research contributes to understanding how national identities are negotiated through both self-representation and external portrayal in international media, revealing the pervasiveness of political discourse in sports coverage.
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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 | 0.001 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".