A Critical Discourse Analysis of News Reports on Sino-US Trade War in The New York Times
Bibliographic record
Abstract
Critical Discourse Analysis is an interdisciplinary approach to the study of discourse regarding language as a form of social practice. As a specific discourse, news discourse is a representation of the journalists’ expression and construction of events, as well as readers’ understanding and cognition of the events reported. It functions as a carrier that transmits ideologies and social values. Recently, news reports on the trade conflicts between China and the US has been the focus of world attention. A study of news reports on Sino-US trade conflicts with Critical Discourse Analysis approach helps interpret the relation between language use and social contexts and reveal ideological significance and power struggle in language. Twenty pieces of news reports on China’s tariff actions on the United States, collected from The New York Times from 2018 to 2019 are studied and the result shows that the use of language in the news texts is not arbitrary, but rather dominated by the medium. The options of lexical expressions in news, the selection of clause types and the position of participants enable the medium to construct a negative image of China and to define China as an unfavorable country. The reasons deciding the language use in this discourse are the tension and balance of the power relation between the U.S. and China in the trade war, and the institution’s favor of the American interest, the American political hegemony and the advocacy of force.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".