“China’s Foreign Aid” and “China’s National Image” in the Eyes of Foreign Media: A Corpus-Based Discourse-Historical Analysis
Bibliographic record
Abstract
China’s foreign aid implements the concept of a community with a shared future for mankind and promotes the common progress of China and developing countries. News about China’s foreign aid released by foreign media is quite significant for overseas audiences to perceive China’s image. This study adopts Ruth Wodak’s discourse-historical approach and corpus-based method to interpret foreign news reports on China’s foreign aid. The macro-level news themes, meso-level discourse strategies and national images, as well as micro-level discourse features are explained with examples. Research shows that news themes reveal the objects, fields and methods of China’s foreign aid. Foreign media widely adopt discourse strategies of nomination, predication, argumentation, perspectivation, intensification and mitigation to shape China as a friendly international donor. A few characteristics of news discourse indicate that several media misunderstand or smear China’s foreign aid, which has a negative impact on China’s international public opinion environment. Therefore, building an effective external voice platform to convey China’s international responsibilities and contributions is necessary, which helps establish a favorable international public opinion environment for China’s development and call on other countries to make efforts to reduce human poverty.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".