The Images of China in TIME: A Corpus-Based Critical Discourse Analysis
Bibliographic record
Abstract
With the help of corpus statistics, this article compares the China-related covers and reports of TIME over its 100 years of existence and divides them into four periods. As a real-time political news publication, TIME has the characteristic of closely matching the development of the times. In different historical periods, China’s image, China-related cover characters and headlines, and the content of China-related reports vary greatly. The image and report on wartime China are more objective and positive, with a more positive tone. In the Redified China phase, the China-related reports were mainly negative, trying to create a chaotic and bloody image of Communist China for Western readers. In the reforming or Changing phase of China, the coverage shifted from the negative terms to a neutral and objective one, unfolding China’s change and development to the Western world. While in the diversified development phase of China, China-related cover stories and terms on TIME were more objective. The characters and images that TIME tries to highlight were also inconsistent in different historical periods, indicating that the shaping of TIME’s China cover image is deeply influenced by the ideology, news values, culture, and worldview differences of the reporters.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.012 | 0.017 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".