Frequency Analysis as a Way of Uncovering News Foci: Evidence from the Guardian and the New York Times
Bibliographic record
Abstract
Institutions or people can express their political stances or attitudes toward a specific topic if they keep using some words rather than others repetitively and consistently. This study uses the corpus linguistic technique of frequency to examine the influence of the country where the newspaper is published on its agenda and coverage using a corpus of about 7 million words of news articles about Libya and Qaddafi in the Guardian (Britain) and the New York Times (the U.S.) from 2009 to 2013. The compiled corpus is divided into three time periods, namely: before, during, and after the 2011 Arab uprisings. The analysis shows that the two newspapers had different news foci/themes in the three investigated time periods, and that they are influenced by the stock of ideas circulating in the culture in which they are working. Both newspapers covered more news of events that draw the attention of the people of the countries where they are located and published. The paper concludes that there is a strong relationship between media and politics where media is a central arena for viewing the political events.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".