Terrorism as an aesthetic signifier: The afterlives of terrorism discourse in Western reactions to wartime suffering
Bibliographic record
Abstract
Abstract What are the legacies of the war on terror? This paper seeks to answer this question through an analysis of vernacular uses of terrorism discourse in political commentary on the Ukraine war. The paper describes how the set of tropes, ideas, and recurrent metaphors that constituted the historical backbone of narratives about terrorism before and after 9/11 is now being mobilised in the context of interstate conflict. Instead of rejecting such deployments of terrorism as lay misappropriations of an otherwise-objective concept, we argue that they evidence the aesthetic force of terrorism discourse in organising our ethical relationship to different experiences of (in)human suffering. The paper advances the concept of terrorism as an aesthetic signifier, to provide two contributions to terrorism studies. First, we argue that narrative approaches to the study of political violence in IR can only move forward if they bypass the field’s traditional framing of terrorism – which we dub the (il)legitimacy trap – and push the boundaries of critique beyond the idea of terrorism as unacceptable violence. Second, we contend that IR scholars must situate the signifiers orbiting the discourse on terror within wider racialised aesthetic regimes dictating the visibility and invisibility of collective suffering. With these two moves, we hope to bring more attention to the question of victimisation in terrorism studies, a field historically focused on perpetrators and the conditions of perpetration of violence.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.044 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".