Changing motifs in digital folklore characters of the Russo-Ukrainian War
Bibliographic record
Abstract
Folklore is the way through which people think and learn, share their knowledge and traditions, form their identity, and make sense of the world around them within a specific group. The folklore developed in the context of Russia’s war against Ukraine has been narrated mostly by means of digital tools across different social media platforms. The most ubiquitous form of digital folklore is memes. The rate and number of memes produced since the start of Russia’s full-scale invasion of Ukraine are unprecedented. New memes are being created and disseminated daily; some events and individuals attract a higher number of memes than others. The study surveys how the images of heroes in memes have been constructed and what character features have been ascribed to them. As their image evolved throughout Russia’s full-scale invasion of Ukraine, different motifs coalesced around them. The article aims to show how such motifs have migrated from one cycle to another, and what types of folklore heroes these memes have constructed in the public imagination.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| 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".