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.
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 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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".