Re-examining the determinants in international news coverage : cross-news-category and cross-country comparisons
Bibliographic record
Abstract
Since the 1960's, communication scholars have conducted numerous studies to identify determinants of international news flow and coverage. Most of these studies have failed to recognize the fact that different news categories are governed by different criteria of newsworthiness. Also, there has been a lack of research that investigates non-Western, especially non-U.S. news media, and a lack of comparative studies examining media in different parts of the world. The current study was designed to address the two problems. Using quantitative content analysis, four newspapers, which were published in Canada, India, China and the United States, were investigated. Their international news outputs in four news categories were compared. The four categories were political news, military news, economic news and news on natural disasters. Two hypotheses were tested: (1) In a newspaper, the determinants of international news coverage vary among different news categories; (2) The determinants of international news coverage in a particular news category are similar among newspapers from different countries. Hypothesis I was generally supported by the data; however, hypothesis II was only partially supported. The results suggest that research should be conducted to understand news-making processes in different news categories. More comparative studies are required to further explore similarities and variations in news values and practices in different 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 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".