Construction of Huawei’s Image in News Reports: Take China Daily and The New York Times for Example
Bibliographic record
Abstract
In recent years, as China’s largest smartphone company, Huawei’s position in the international market has gradually increased and received widespread attention from foreign media. The rapid development of China’s impact on the hegemony of the US has also changed the direction of US media’s reporting on Chinese companies. At this stage, it is meaningful to study the image of Huawei in both Chinese and US media reports. Therefore, based on the corpus approach and critical discourse analysis, this paper builds two corpora of China Daily (576 reports with 438,261 words) and The New York Times (429 reports with 347,025 words). It is found that (1) both sides acknowledge that Huawei ranks top in world telecommunication technology, particularly in the 5G network; (2) two newspapers focus on different aspects in their reports. For the Chinese media, Huawei’s technological prowess, innovation capacity in the global market, cooperation with many other European and African countries are given more attention, while for the American media, more focus is shifted to Huawei’s threat to national security; (3) two newspapers hold different attitudes towards the rise of Huawei. China Daily’s positive construction of Huawei’s image is obvious. While for the American media, the Trump administration is more likely to project a threatening image of Huawei; (4) the reporting frameworks and the styles of materials selected differ in two newspapers. China Daily’s framework concentrates on “Huawei” itself, while The New York Times tends to construct a reporting framework from multiple perspectives from the third-party.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".