The Russian – Ukrainian War: Persistence of Frames and the Media Issue-Cycles
Bibliographic record
Abstract
The Russian invasion of Ukraine drew unprecedented media attention all over the world due to its dramatic character and significant consequences. Beside the heightened interest, the media coverage also reflects the nature of media systems in democratic and authoritarian societies. This paper aims at testing whether there are some common features between media systems such as issue-cycle regularities with the interest in the event waning over time. This study also explores how issue specific frames serve the goals of different media system. The American news outlet CNN and the Russian news source gazeta.ru were selected to account for media system differences. The time frame encompassed the first five days of the conflict, and the most recent five days for the study. The number of articles dedicated to the war significantly decreased over the half year period for both outlets. The number of key terms used for the war decreased over the same time at CNN, but not at gazeta.ru. Framing analysis demonstrated that the use of frames depended more on the goals of propaganda than on the actual events in the authoritarian media system. The use of frames at CNN was more consistent with the real situation on the ground underlying the fact that freedom of speech is more conducive to reporting the truth.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 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) | 0.003 | 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".