Two decades later, same rhetoric: how the discourse on terrorism risks eroding human dignity
Bibliographic record
Abstract
This article examines the official political discourse that immediately followed the Hamas attacks of 7 October 2023, and compares it to that which followed the al-Qaeda attacks of 11 September 2001. These two series of attacks resulted in massive human losses amongst the civilian population and took place in the territories of two key allies, the United States and Israel, and major trends in their rhetorical approach of terrorism, and their reaction to it, can be highlighted. Through critical discourse analysis and the lens of critical terrorism studies, it highlights five dominant rhetorical patterns shared by political leaders in both contexts: polarisation and Manichaeanism; retribution and revenge; existential threat framing; assimilation of adversaries to broader populations; and dehumanisation. These rhetorical strategies, the article argues, have tangible consequences—not only shaping public sentiment and legitimising extraordinary wartime measures, but also undermining the principles of international humanitarian and human rights law. The analysis illustrates how such discourse can blur the line between combatants and civilians, fostering conditions in which violations of international law are more likely to occur or be tolerated. Drawing parallels between the U.S. “war on terror” and Israel’s military response in Gaza, the article warns of the continued risks posed by invoking the term “terrorism” as a legal and moral justification. Ultimately, it calls for greater scrutiny of counterterrorism discourse and urges the international community to resist narratives that obscure legal boundaries and compromise human dignity.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.013 | 0.041 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".