Bibliographic record
Abstract
Throughout the Second World War and the post-war period, the city of Chernivtsi was transformed from a multiethnic and borderland urban microcosm into a culturally uniform Soviet socialist city. As the Soviets finally took power in this onetime capital of a Hapsburg province in 1944, they not only sponsored further large-scale population transfers but also "repopulated" its history, creating a new urban myth of cultural uniformity. This article examines the connection between war commemoration in Chernivtsi in the era of post-war, state-sponsored anti-Semitism and the formation of collective memory and identities of the city's post-war population. The images of homogeneously Ukrainian Chernivtsi and Bukovina were created through the art of monumental propaganda, promoting public remembrance of certain events and personalities while making sure that others were doomed to oblivion. Selective commemoration of the wartime events was an important tool of drawing the borders of Ukrainian national identity, making it exclusivist and ethnic-based. Through an investigation of the origins of the post-war collective memory in the region, this article addresses the problem of perceived discontinuity between all things Soviet and post-Soviet in Ukraine. It demonstrates that it is, on the contrary, the continuity between Soviet and post-Soviet eras that defines today's dominant culture and state ideology in Ukraine and particularly in its borderlands.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".