Cyborgs vs. Vatniks: Hybridity, Weaponized Information, and Mediatized Reality in Recent Ukrainian War Films
Bibliographic record
Abstract
Focusing on Akhtem Seitablaiev’s blockbuster Kiborhy: Heroi ne vmyraiut' (Cyborgs: Heroes Never Die, 2017) and Sergei Loznitsa’s auteur production Donbass (2018), this article argues that the latest cycle of Ukrainian war films merits critical attention as an astute record of conspicuous social transformations in today’s Ukraine and as a medium that presents an original perspective on the hybrid nature of modern war and its mediatization, the latter being a relatively new theme in war films broadly defined. The article uses post-colonial and cyborg theories of hybridity, Baudrillard’s concept of simulacra, and the Marxist notion of “false consciousness” to illustrate how post-Soviet, post-colonial, and post-truth aspects of war-torn Ukraine conflate in Seitablaev’s and Loznitsa’s works to bring to the fore a recent shift in the nature of warfare itself. As the two films unequivocally demonstrate, the latter is defined not so much by high-tech armed operations and direct annihilation of the opponent as by contactless warfare, as well as its consequences for those directly influenced by it.
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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 | 0.005 | 0.008 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".