Critical Discourse Analysis of Iran-China Relations Through Football Headlines
Bibliographic record
Abstract
Iran and China are getting closer day by day, from economic to political to social relations. Since media plays a significant role in shaping norms, social relations, and values in society, it is not a neutral medium to propagate information only. Social and political elites, as well as political fractions, use media to shape public opinion with the least cost. Therefore, this paper attempts to answer the question as to which ideologies, social and political thoughts Iranian football headline-writers attempt to convey to the wider public in Iran. To answer this question the main hypothesis of the paper posits that Iranian political and social elites are conveying a much more economic dimension of China, downplaying the political dimension of China, including the mounting political oppression inside China and Hong Kong, and the dramatic situation of Chinese Muslims. This paper approaches the abovementioned question under the framework of critical discourse analysis proposed by Norman Fairclough (1989). With regards to the methodology of the paper, a qualitative approach has been adopted, availing itself of different headlines of Iranian sport. Findings of the paper indicate that elites in Iran are proposing to the public a much rosier image of china due to closer political and economic ties between the two countries.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 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".