The geography of foreign news on television
Bibliographic record
Abstract
Since the advent of television in the middle of the 20th century, news has been an essential ingredient in TV programming. Often these newscasts are the most heavily viewed programmes, and by and large they are the main source of information for many people. This is particularly true for news from other countries and regions in the world. This immense significance of TV news has made it an important field in communication research. The article presents a new study that is formed from a multinational project. The project investigated foreign TV news in 17 countries from five regions in the world: Belgium, Brazil, Canada, Chile, China, Egypt, Germany, Hong Kong, Israel, Italy, Japan, Poland, Portugal, Singapore, Switzerland, Taiwan and the United States of America. The data of the content analysis in all these countries in 2008 contain over 17,500 news items. The analysis concentrates on ‘news geography’, a term that is used to describe the extent to which the countries of the planet are represented in TV news. The results show a complex, multifaceted picture of foreign news reporting in the world. This multifaceted picture demands multi-causal interpretation. Several factors are discussed, i.e. the types of countries, their political order and integration into the international system, trade, different degrees in political power, but also historical connections, cultural ties, etc. Principally, the foreign news outlet depends on the selection criteria of journalists. On the whole the findings seem to question the world’s globalization, which is often taken for granted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".