Russian Propaganda from V to Z: Projecting Banal and Everyday Nationalism in Unsettled Times
Bibliographic record
Abstract
Abstract How do autocracies use nationalism to normalize and contain unsettled times? The full-scale invasion of Ukraine in 2022 marked a decisive point in Russia’s politics from which there could be no return to an antebellum normality. Rather than attempt to mobilize the Russian public to war, state-run television sought to normalize the war as a banal reality for domestic audiences. Drawing on a content analysis of 1,575 reports from the state-run First Channel [ Pervyi Kanal ] from 2022 to 2024, this article argues that the Ukrainian regions occupied by Russia — the so-called “new regions” — are crucial to this strategy through their incorporation into banal nationalist depictions of Russia. In turn, televised depictions of residents in the “new regions” confer emotional weight and moral examples for ordinary Russians through their everyday practices: their fortitude in voting for Putin despite ongoing attacks; through their shared excitement in acquiring routine aspects of daily life from passports to pensions; and through their embodiment of Russia’s future. In the process, media depictions normalize imperial nationalist justifications for Russia’s occupation of Ukrainian territory in terms of the distinctiveness of the Russian people, Russia’s civilizing mission, and presentation of its war as defensive.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".