Cursed Russians and Armed Saints: “Angry Folklore” and the Ethics of Precarity in Response to the 2022 Russian Invasion of Ukraine
Bibliographic record
Abstract
Abstract The full-scale invasion of Ukraine by Russia has generated a surge of “angry folklore,” namely, cultural expressions imparted with themes of hostility, rage, and aggression. Some of this expression fits the established definitions of “hate speech,” thus raising ethical questions about the performance, perception, and documentation of this folklore. The present study explores these questions from the positions of predominantly US-based English speakers watching from afar and of Ukrainians closely affected by the war. It shows the importance of considering angry folklore with compassion and empathy by focusing on the subject position of the performers and the degree of their precarity. We argue that expressions of anger from the position of extreme material precarity must be considered differently than similar expressions from positions of low or no material precarity. Both through the reality of direct violence and through the more subtle mechanisms of “systemic vernacular imperialism,” more and less precarious subject positions change what is at stake in the performance of angry folklore.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".