Bibliographic record
Abstract
From the very first days of their large-scale attack on Ukraine in February 2022, the Russian invaders have made exceptional efforts to interact with the war memorial landscape of the newly occupied territories. This landscape consists of tens of thousands of monuments, mostly in small towns and villages, commemorating the Second World War and other conflicts, including Ukraine’s resistance against Russia since 2014. The Russians have destroyed some of these memorials, renovated others, and built new monuments amid continued fighting. They also used war memorials in countless propaganda photos and videos aimed for a domestic audience and largely escaping Western attention. Why this fervor? Gabowitsch and Homanyuk draw on unique sources to trace the logic of Russian monument policies in occupied Ukraine. Mykola Homanyuk spent several months in occupied Kherson and collected sources on the ground, often at considerable risk to himself. This exceptional wartime on-site ethnography was complemented by systematic real-time data collection from online sources, many of which have since disappeared. The book shows how Russian invaders believed their own propaganda about Soviet war memorials being mistreated in Ukraine, and what they did when they discovered well-maintained monuments on the ground. More generally, it also discusses the link between monuments and territorial claims by irredentist empires.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".