Image of China in Afghanistan News Discourses: A Corpus-Based Critical Discourse Analysis
Bibliographic record
Abstract
Afghanistan and China have a long credible history and reliable relationship. Afghanistan-China’s friendship has been verified to be the model of cooperation between two neighboring countries. Both states have strong historical, cultural, social, economic, and political relations together. The relationship emerged in front of the world in 1955, when both the countries signed an economic treaty, known as the “Treaty of Economic and Technical Cooperation.” The study aims to investigate the image of China in the Outlook English newspaper of Afghanistan, whereas China’s recent development in trade and the economic rise around the globe has given new birth to the cooperativeness between both the countries. The current trade has reached up to $700 million between both the countries. Thus the study identifies the facts from the corpus-based analysis that the frequency of economic relations between Afghanistan and China has risen due to a significant trust and friendly relation with each other. Moreover, the success in economic trade depends on the positive perspective of an excellent historical background and political relationship in the history of one’s country in another. Both countries’ good historical friendships reveal a significant positive image of China in Afghanistan’s Outlook English newspaper. The occurrences of development, China, cooperation, economic and industrial cooperation reveal China’s interest and friendly relations moving towards Afghanistan in particular. Therefore, such engagements of China with Afghanistan will bring economic development and make a better security situation in Afghanistan.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".