Fighting for the Soviet Union 2.0: Digital nostalgia and national belonging in the context of the Ukrainian crisis
Bibliographic record
Abstract
This paper focuses on the use of Soviet-era symbols, myths, and narratives within groups on VKontakte social media site over the initial stage of the Ukraine crisis (2014–2015). The study is based on qualitative content analysis of online discussions, visual materials, and entries by group administrators and commentators. It also applies link-analysis in order to see how groups on social media are interrelated and positioned online. It reveals that these online groups are driven primarily by neo-Soviet myths and hopes for a new version of the USSR to emerge. Over time, the main memory work in these groups shifted from Soviet nostalgia and “pragmatic” discourse to the use of re-constructed World War II memories in order to justify Russian aggression and to undermine national belonging in Ukraine. Reliance on the wartime mythology allowed for the labelling of Euromaidan supporters as “fascists” that should be eliminated “once again.” This powerful swirl of re-created Soviet memories allowed effective mobilization on the ground and further escalation of the conflict from street protests to the armed struggle.
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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 | 0.002 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".