Producing Feminist Discourses in the Debris of Destruction: Maria Kulikovska’s Response to War in Let Me Say: It’s Not Forgotten
Bibliographic record
Abstract
The Ukrainian–Crimean artist Maria Kulikovska’s artistic practice has addressed war in Ukraine since the Annexation of Crimea and outbreak of war in the Donbas regions of Ukraine in 2014. In 2019 she created the video-performance Let Me Say: It Will Not Be Forgotten that responds to the ways artworks and women’s bodies are targeted by derisive retaliation and physical attacks during periods of political instability. Informed by explorations of feminism in post-Soviet countries, theories of prosthetic memory, and destruction art of the 1960s, I argue that Kulikovska does not let the destruction of her artwork silence her, but, rather, she uses destruction as a strategy to take control of oppressive forces. In their place, I argue that Let Me Say: It’s Not Forgotten demonstrates subjective and complex ways of building resilient feminist presents and futures that overcome oppressive violence and testify to continual perseverance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.033 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| 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".