Writing around War: Parapolemics, Trauma, and Ethics in Ukrainian Representations of the War in the Donbas
Bibliographic record
Abstract
The article considers a range of literary texts about the war in Donbas and argues that one of the primary representational strategies employed by Ukrainian writers has been the use of “parapolemics.” The article operates with Kate McLoughlin’s definition of this term as a focus on the “outskirts” of armed conflict, but also relates the idea to concepts drawn from trauma studies. While, on the one hand, the use of parapolemics may be a way of avoiding direct representation of wartime violence and death, the opportunities it affords are extremely valuable: focusing on the “backstage” of war and eschewing direct representation of violence allows writers to explore otherwise marginalized, and highly complex, dimensions of wartime experience. At the same time, connecting the parapolemic approach to ideas taken from trauma theory, particularly relating to empathy and responsibility, allows us to understand how parapolemics provide a way of reflecting both on the ethics of representing war and the of self-other relationships that arise in wartime.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.022 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".