Construction of the Belt and Road Initiative in Chinese and American Media: A Critical Discourse Analysis Based on Self-Built Corpora
Bibliographic record
Abstract
The Belt and Road Initiative reflects China’s ascendance in the world arena. Since its inception, this initiative has received great attention from Chinese and American media. This study applies the critical discourse analysis (CDA) method to investigate the mainstream media construction of the Belt and Road Initiative. Based on the “Lexis Advance” database, a sample of news reports dated between January, 2017 and November, 2018 were selected to build two corpora of China Daily (368 reports with 232 550 words) and The New York Times (154 reports with 106 401 words). Assisted by the two self-built corpora and the corpus software AntConc 3.2.4, the study probes into the similarities and differences between Chinese and American reports in terms of high-frequency words, collocation networks, concordance lines and concordance plots. The findings are (1) both the Chinese and American media pay great attention to the contribution of this initiative to the world economy. (2) Chinese media emphasize the concrete measures of this initiative, while American media focus on its political influence. (3) Chinese media use explicit positive vocabulary to appraise the achievement of this initiative, while American media use explicit negative vocabulary to express Trump administration’s skepticism about this initiative. (4) American government’s attitudes towards this initiative have gradually changed since Trump came to power. Though negative comments still exist, the positive voice has increased.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".