Gardens of Tolerance: Ukrainian Women Artists Reflect the War in the Donbas
Bibliographic record
Abstract
With ongoing war in the Donbas, war narratives and war images saturate public media in Ukraine, the discourse contaminated by ideological remnants of the Soviet World War II cult and by fake news. Art that deals with war wounds can subvert the familiar visual language of war propaganda, where the suffering of victims is a mere pretext for touting the inevitable triumph of the heroes. Currently in Ukraine, the most prolific art in this regard is produced by women-artists who address the trauma of war through painting and installations that offer highly personalized accounts. Often touching upon extreme circumstances, their art is about tolerance, both in terms of endurance and of the mutual understanding necessary for cohabitation. Alevtyna (Alevtina) Kakhidze’s ongoing performance creates an opportunity to comprehend the war in the Donbas from multiple perspectives, including that of a gardener. She associates the tending of plants with her mother who died on occupied territory, refusing to leave her garden. Mariia (Maria) Kulikovs'ka’s sculptures serve as shooting targets for separatists in the occupied centre of contemporary art in Donetsk. Vlada Ralko’s paintings of tortured bodies become a metaphor for scars garnered by a war that remains close to home. Paintings and sculptures by Maryna Skuharieva (Skugareva) and Anna Zviahintseva (Zvyagintseva) address the ruin of representation inflicted by war, and the conceptual performance by Liia (Lia) Dostlieva and Andrii Dostliev contemplates the healing process of war wounds. Neither making spectacle from the “pain of others” nor deeming it unrepresentable, this art seeks emphatic alternatives to traditional war narratives.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.020 | 0.011 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".