Documenting war in Ukrainian comics: shifting from soldiers to civilians
Bibliographic record
Abstract
Against the backdrop of the Russian–Ukrainian war, Ukrainian comics have emerged as a potent medium for depicting the ongoing conflict. They blend informational, political, cultural, and artistic elements, contributing to a multi-dimensional media discourse. They also serve as a means of documenting the war’s reality, becoming an integral part of cultural memory. The Ukrainian documentary comic series Kiborhy (Cyborgs) and the comic magazine INKER play a significant role in this regard. When comparing the earlier Kiborhy to the more recent INKER comics, a discernible shift in narrative focus can be observed: whereas Kiborhy portray heroic Ukrainian soldiers, INKER’s narratives centre around the everyday heroism of ordinary civilians. As opposed to Kiborhy, INKER utilizes intimate storytelling, often employing first-person narratives and personal reflections to convey the emotional impact of war without presenting any explicit violence. This shift also marks a departure from dehumanizing portrayals of the enemy as seen in Kiborhy, to a more nuanced approach where the adversaries are not explicitly named in the storytelling. This transition has diversified the range of narratives and characters, enabling social commentary and transforming Ukrainian comics into a more inclusive and diverse medium.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".