From geopolitical fault-line to frontline city: changing attitudes to memory politics in Kharkiv under the Russo-Ukrainian war
Bibliographic record
Abstract
The article investigates changing attitudes to memory politics in Ukrainian city of Kharkiv. In February 2022, with the outbreak of the full-scale Russo-Ukrainian war, this geopolitical fault-line city became a frontline city with significant potential outcomes for urban identity and local geopolitical preferences, including attitudes to the national memory politics. The research is based on the comparative analysis of the two surveys among residents of Kharkiv, conducted in spring-summer 2018 and in autumn 2022 – before and after the full-scale war. The results of the surveys are analysed by means of descriptive statistics and binary logistic regression. Additionally, two focus groups were held in order to receive additional justification when interpreting the results of the survey. The research shows that the attitudes to Ukrainian nation-centric memory narrative, including both decommunisation and decolonisation, have significantly improved. Nevertheless, public attitudes to the memory politics in the frontline city are highly reflexive in nature and deeply embedded in the context of the ongoing war. The geopolitical divide, which existed before the war, has largely transformed into a cultural one, namely heterogeneity of attitudes to the Russian cultural heritage in the city. This softened albeit still existing divide has, to some extent, materialised in physical space and runs between the ardent supporters of decommunisation and decolonisation that massively fled from the atrocities of the war and their opponents who at most choose (or were obliged) to stay in the front-line city. The study reveals that military conflicts may either activate hidden geopolitical divides in geopolitical fault-line cities or contribute to their transformation or even disappearance.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".