Protagonists of terror: the role of ludology and narrative in conceptualising extremist violence
Bibliographic record
Abstract
This paper is a conceptual exploration of whether terrorists’ self-perception as (anti-)heroes, playing characters drawn from their internalised narratives within a ludic framework, offers a better understanding of the mechanism which translates extremist ideologies into violent action. Applying narrative theory to the stories told through acts of communicative terrorism, I argue that viewing terrorists as their own ‘protagonists’ offers an improved understanding of terrorism. Given the growth of extreme right-wing terrorism and the increasing prevalence of individuals acting as characters, I further incorporate existing research in ludology and ‘ludic terrorism’ to evaluate the concept of a terrorist as a ‘ludonarrative protagonist’. This paper contributes to the methodology of terrorism studies by proposing a way of conceptualising terrorist actors harmonised with existing psychological and behavioural research. I also offer practical implications for counter-terrorism efforts. Adopting this more nuanced framework will better equip counter-terrorism practitioners for preventative engagement with (potential) terrorists by centring counter-narratives and the construction of roles which reinforce cognitive barriers to violent action. This research provides an alternative explanation for why and how individuals engage in terroristic violence, recognising the emergence of an increasingly decentralised terrorism ecosystem.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.025 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".