Rhetorical Construction of COVID-19 News in Chinese Context—A Corpus-Based Study
Bibliographic record
Abstract
As a language practice, news discourse is rhetorical that responds to a given circumstance and conveys values and ideas to the target audience with specific goals. This study examined how the English official website in China represented COVID-19 news to affect the public under the rhetorical framework. Using Sketch Engine as the tool for data analysis on a self-built corpus, this study carried out a quantitative analysis of the linguistic features of the COVID-19 news on In Zhejiang, the official English website of China’s Zhejiang Province. Then, based on the data obtained from corpus analysis, this study applied both modern and classical rhetorical theories to conduct a qualitative analysis of the rhetorical mechanism, including rhetorical invention, rhetorical strategies, and style, of the COVID news on the website of In Zhejiang. The results showed that the COVID-19 news was a positive response to the COVID situation in Zhejiang province and China with the purposes of mobilizing the citizens and establishing favorable images of the government and the Zhejiang people. Meanwhile, the news was constructed within its cultural context and Chinese journalism conventions, making it more adapted to the home audience and limiting its appeal to a global audience and effectiveness for worldwide communication. By demonstrating that news discourse is a rhetorical construction, this study provides valuable insights for the news media, particularly Chinese official media, on how to approach the audience rhetorically to improve the persuasiveness of their news reports, as well as a new theoretical framework for the research of public health crises.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".