Coverage of Post-Communist Countries by ABC, CBS and NBC: Politics of Miscommunication
Bibliographic record
Abstract
This paper analyzes the coverage of major post-communist countries, particularly their politics, by main television networks in the United States. The question is whether there are significant qualitative and quantitative differences in the representation of East-Central European and post-Soviet countries on U.S. television programming. The related question is whether political factors, such as relations with the United States and national phobias, affect the television coverage of the post-communist countries. Although the question of political biases in coverage of post-communist nations by mass communications media in the United States has been raised by a growing number of politicians and journalists, there is a lack of academic studies on this issue. This paper uses quantitative and content analyses of transcripts of news programs from the most-watched U.S. TV networks: ABC, CBS, and NBC from 1998-2008. The following news programs are examined: World News, Nightline, and 20/20 on ABC, CBS Evening News, CBS Sunday Night News, 60 Minutes, 60 Minutes II, and 48 Hours on CBS, and NBC Nightly News, and Dateline on NBC. A keyword search of transcripts in the Lexis-Nexis database is employed to identify specific broadcasts and news stories that focused on major post-communist countries, such as Russia, Ukraine, Poland, Romania, Bulgaria, the Czech Republic, Hungary, Kazakhstan, Belarus, and Georgia. The analysis shows significant differences in quantity, the proportion of political content, and quality of American television coverage of the post-communist countries. Such political issues and events as U.S.-Russia relations, the war between Russia and Georgia over South Ossetia, major terrorist acts in Russia, the poisoning of Alexander Litvinenko (a former Russian secret service employee) in the United Kingdom, Chechen Islamic terrorism, the poisoning of Viktor Yushchenko (the future president of Ukraine), the “Orange Revolution” in Ukraine, and the deployment of the U.S. missile system in the Czech Republic and Poland, were among top stories dealing with post-communist countries in 2004-2008. Other leading topics included the following: Russian crime, Chernobyl (Chornobyl) disaster and its effects in Ukraine and Belarus, child pornography in Belarus, Borat movie about Kazakhstan, Polish Pope John Paul II, the Holocaust and World War Two in the Czech Republic, Bulgarian and Hungarian immigrants, and Dracula and sex slaves and sex slavery in Romania. The analysis produces evidence of systematic biases in representation of some post-communist countries, particularly, Russia, Ukraine, Kazakhstan, and Georgia. The study presents results of the analysis regarding the role of such political factors as relations of post-communist states with the United States on their coverage by the U.S. TV networks.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".