“Moskal's,” “Separs,” and “Vatniks”: The Many Faces of the Enemy in the Ukrainian Satirical Songs of the War in the Donbas
Bibliographic record
Abstract
This article examines representations of the enemy in the Ukrainian satirical songs pertaining to the Russo-Ukrainian war in the Donbas. I focus primarily on the output of Orest Liutyi (the stage persona of Antin Mukhars'kyi) and the semi-anonymous Mirko Sablich (Mirko Sablic) collective. Using the method of multimodal discourse analysis, I examine how the enemy opposing the Ukrainian Army is portrayed in the song lyrics and the accompanying music videos. Considering the complex nature of the conflict and the lack of uniformity in the backgrounds of the warring parties, I am particularly interested in who and why is identified as the enemy in the songs. The enemy appears in several guises: “moskal's”—Russian or pro-Russian aggressors from outside Ukraine; “separs”—Ukrainian collaborators who support, often through military efforts, the separation of the Donbas from Ukraine; and “vatniks”—passive anti-Ukrainian individuals who live in Ukraine and whose inaction is perceived to be harmful to Ukraine’s wartime efforts. Whereas these songs call upon Ukrainians to repel the external enemy (“moskal's”) in armed combat, no clear strategy is suggested for how the internal enemies (“separs” and “vatniks”) should be dealt with or, in some cases, even identified. As a result, Liutyi and Sablic, while positioning themselves as “counterpropaganda” projects, risk labelling as “the enemy,” and thus alienating, the audiences most susceptible to propaganda, who could otherwise benefit most from their myth-debunking efforts.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".