Bibliographic record
Abstract
When Russia invaded Ukraine in 2014, the Russian media ran what I propose to call a simulation of “non-invasion”—a spectacle aimed to distance Russia from the war. This essay explores activist art resistance against this simulation. Specifically, I discuss three art projects that were staged during the first, most violent year of the Russian-Ukrainian conflict: Mariia (Maria) Kulikovs'ka’s performance at “Manifesta 10” in St. Petersburg, Serhii Zakharov’s guerrilla installations on the streets of occupied Donetsk, and Izolyatsia’s #onvacation occupation of the Russian pavilion at the 56th Venice Biennale. These art projects, I argue, not only attacked the simulation from the outside as independent entities, but, by penetrating the simulation on site and online, they disrupted it from within. I offer three reasons to support this claim. First, these art projects superimposed images of the invasion over the physical sites where the “non-invasion” simulation dwelt and, in this way, not only made the war visible but also produced “a glitch in the matrix” effect—a conflict within the simulation visual regime that was inconsistent with its concealment function. Second, they “hailed” (in Louis Althusser’s terms) actants of the simulation as subjects of Putin’s regime, provoking suppressive reactions that proved Russia’s participation in the war—which the simulation, thus, failed to downplay. And third, with carefully orchestrated strategies of online outreach to the public, these art projects attached themselves to the media dimension of the simulation, making the simulation’s media proliferation work against itself.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".