Does News Platform Matter? Comparing Online Journalistic Role Performance to Newspaper, Radio, and Television
Bibliographic record
Abstract
The shifting role of journalism in a digital age has affected long-standing journalistic norms across media platforms. This has reinvigorated discussion on how work in online newsrooms compares to other platforms that differ in media affordances and forms. Still, more studies are needed on whether those differences translate into distinct practices, especially when examining cross-national studies. Based on the second wave of the Journalistic Role Performance (JRP) project, this article reports the findings of a content analysis of 148,474 stories produced by 365 media organizations from 37 countries, comparing the performance of journalistic roles in online newsrooms to three other types of media—TV, radio, and print. The paper analyzes if journalistic roles present themselves differently across platforms, and if these differences are constant or they vary across countries. Results show that there are measurable differences in role performance in online journalism compared to other platforms. Platform had a significant impact, particularly in terms of service and infotainment orientation, while the implementation of roles oriented toward public service was more similar. Additionally, country differences in the relationship between role performance and platforms mainly emerged for roles that enable political influence on news coverage, with differences in the relationship between online vs. traditional platforms appearing to be distinct features of the specific political system.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 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".