(After) Five Years of War in the Donbas: Cultural Responses and Reverberations
Bibliographic record
Abstract
This special issue is dedicated to the study of an important phenomenon that has been taking place in Ukraine for what is now approaching a decade.The 2013-14 Revolution of Dignity was quickly followed by Moscow's annexation of Crimea and then by a war between Russia and Ukraine in the Donbas region.The war greatly impacted various aspects of life in Ukraine in the past eight years including its profound effect on Ukrainian culture.Ukrainian artists, who had been leading a vigorous, varied, and long-awaited free explosion of creative achievements in Ukraine since the country's independence in 1991, were roused and galvanized by the sudden appearance of war in their land.The war became the subject of artistic projects by many of Ukraine's leading filmmakers, writers, visual artists, and musicians, and also brought to light the work of new creative voices.These artists developed new approaches while providing fresh perspectives on many issues that had also, in fact, been the focus of many of the notable cultural achievements over the past thirty years, including questions of identity, memory, gender, and displacement.Borders and borderlands, concepts intrinsic to Ukraine's name, once again acted as sites where these topics were explored.The impact of the Russian-Ukrainian war on Ukrainian culture has now, correspondingly, become a subject of scholarly study.The poems, novels, plays, films, installations, performances, paintings, and songs that have emanated from Ukraine are increasingly analyzed at conferences and in articles in various global academic forums.One such assembly was the conference Five Years of War in the Donbas: Cultural Reflections and Reverberations, which was held at Columbia University on 1-2 November 2019.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".