Sources in the News
Bibliographic record
Abstract
In analysing the news media's role in serving the functions associated with democratic citizenship, the number, diversity and range of news sources are central. Research conducted on sources has overwhelmingly focused on individual national systems. However, studying variations in news source patterns across national environments enhances understanding of the media's role. This article is based on a larger project, “Media System, Political Context and Informed Citizenship: A Comparative Study”, involving 11 countries. It seeks, first, to identify differences between countries in the sources quoted in the news; second, to establish whether there are consistent differences across countries between types of media in their sourcing patterns; and, third, to trace any emergent consistent patterns of variation between different types of organization across different countries. A range of findings related to news media source practices is discussed that highlights variations and patterns across different media and countries, thereby questioning common generalizations about the use of sources by newspapers and public service broadcasters. Finally, a case is made for comparative media research that helps enhance the news media's key role as a social institution dedicated to informed citizenship.
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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.005 |
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".