(De)legitimization strategies of Russia’s war against Ukraine: a case study of Russian television programs
Bibliographic record
Abstract
Television plays a central role for the Putin regime in legitimizing the current war against Ukraine. This article analyzes how the war against Ukraine was officially legitimized on Russian state television in the first year since the beginning of the full-scale invasion in February 2022. As a counterweight to this official discourse, the article also examines how the last independent and exiled TV channel, Dozhd′ (TV Rain), attempts to delegitimize the narratives disseminated by the state media. Drawing on the legitimization strategies theory by Theo Van Leeuwen and Antonio Reyes, the study qualitatively analyzes selected TV broadcasts. The focus is on whether and which (de)legitimization strategies can be found on Russian (state) television. The results demonstrate that legitimization strategies are omnipresent on state television and that visual and verbal information is used both to emphasize official statements and to affect viewers emotionally. Furthermore, the study reveals the repetitive nature of legitimization strategies on Russian state television and thus identifies legitimization as an essential component of manipulative political persuasion, that is, state propaganda.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".