Russia’s Portrayal in the Mirror of International Mass Media: The Role of Cultural Context
Bibliographic record
Abstract
The analysis of cultural context in media texts can contribute to understanding how national images are constructed in the international media discourse. The image of a country is better understood by the audience of another country when it is introduced through familiar cultural concepts and well-known experiences so that specific, culture-bound elements of the other culture are brought closer to the target audience.The research provides linguo-cultural analysis of Russia’s portrayal in political media discourse in English-speaking countries drawing on the approach to political discourse as the process of production and interpretation of a text in meaningful political, social and cultural context.The study is aimed at exploring British and U.S.A. mass media to reveal typical features of the English-language political discourse concerning Russia and to find out how Russia’s image is constructed. In the course of the study we examined culture-bound lexicon in texts of various genres of political discourse in mass media focusing on Russia. Further, the use of Russian culture-bound items without translation in British and American mass media was analyzed, and such items were classified into categories according to their contextual functions.The results indicate that Russia is deeply integrated into the cultural context of the English-speaking audience; it can be said that Russia’s image in the Anglophone political media discourse is outlined with the aid of various cultural-bound associative, connotative and metaphorical links which are familiar for native readers and serve them as a bridge facilitating their understanding and interpretation of Russian culture.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 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".