Memeing war: the use of humor for hope, resistance, and forging the nation
Bibliographic record
Abstract
Much like wars waged in real life on battlefronts, memes battle online for discursive supremacy. Memes were shaping online narratives in the early months of the massive, renewed Russian invasion of Ukraine in 2022. These memes provide hope, develop solidarity, and reinforce a Ukrainian national identity in a context where citizens fight for their very survival. Memes, we argue, enable assailed citizens to call upon a reinvigorated nationalism to resist invading forces. The memes ridicule the enemy, allay fear of the invading foe and affirm that Ukrainians are not Russian. The disparagement of Russians thus encourages the people of Ukraine to hold steadfastly against the invaders as they refuse to be incorporated into a ‘Russian land’ against their will. Memes are central to the nation and nationalism, seeking to shape the outcome of the war to ensure the continued existence of the Ukrainian state and a Ukrainian nation through the mobilization of nationalism using pictures and words shared online.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.027 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".