A Corpus-Based Critical Discourse Analysis of Chinese and American News Coverage on Climate Change
Bibliographic record
Abstract
As the 27th Conference of the Parties to the United Nations Framework Convention on Climate Change (COP27) convened, the climate issue remained a focal point of coverage in both Chinese and American mainstream media. This paper seeks to explore the linguistic features of news discourse of climate change in China and the United States and the discursive strategy applied to strengthen national ideologies. Taking the reports on climate change in China Daily and Los Angeles Times within 2 months after COP27 as the research corpus, this paper examines the language features, the discursive processes of the text and possible social factors through a combination of corpus linguistics(CL) and critical discourse analysis(CDA) under the framework of Fairclough’s Three-Dimensional Model of Discourse. The finding suggests that both countries enhance national ideologies by constructing a responsible national image through objective climate reporting. Chinese media tends to focus on national efforts to improve ecological environment and global cooperation, while US newspaper attaches importance to climate security and pursues a leading position in global ecological governance.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.011 | 0.015 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".