Argumentation and Aggression: About Maps and Poems in the Russian-Ukrainian Conflict
Bibliographic record
Abstract
Argumentation as a function of human communication, and aggression as a feature of communicative behaviour, seem to be contrary to each other. Argumentation should be understood as the regulation of dissent based on rational arguments, whereas aggression can be seen as the manifestation and intensification of dissent. But the boundaries between rationality and irrationality, as well as between the regulation and the manifestation of dissent are often vague. Therefore, not only hate speech but also seemingly rational argumentation can be motivated by aggression and can lead to aggression. In the present study, this intertwinedness of argumentation and aggression is shown in the current Russian-Ukrainian conflict, where we can find the use of aggressive theses, reasons, and aggressive arguments in different semiotic and textual expressions: not only in political statements, but also in poetry and in multimodal forms like political maps. Combining argumentation theory with a case study of aggressive argumentation in the Russian-Ukrainian conflict, the paper presents several forms of intertwinedness of argumentation and aggression. The research is mainly based on maps as a type of popular geopolitics, in which the aggressive thesis of the non-existence of Ukraine is provided. The study also considers the poeto-political war around Anastasiia Dmytruk’s poem “Nikogda my ne budem brat'iami” (“Never ever we will be brothers”). Responses to Dmytruk’s thesis provoke not only disagreement but also negative and positive-negative agreement, which means that the opponent agrees with the thesis but rejects the reasons of the argument, or s/he agrees with the thesis and the reasons but evaluates them in a contradictory way. Whereas the analysis of maps shows mainly the performing of aggressive theses, the analysis of the poeto-political war highlights how reasons are provided in an aggressive communication frame.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".