Bibliographic record
Abstract
Abstract Why do some jihadist organizations engage in beheadings while others do not? Although beheadings have become a signature tactic of the contemporary global jihadist movement, I show that most jihadist groups perpetrate few or no beheadings and only a minority have adopted beheading as a consistent part of their repertoire of violence. Such variation exists even among ideologically similar ‘Salafi-jihadist’ groups, suggesting that ideology alone cannot explain why such violence occurs. Instead, I argue that the use of beheadings is shaped by a combination of local strategic context and transnational ties. Beheadings are strategically useful to jihadist groups engaged in insurgency as a means of deterring civilian collaboration with the enemy, demoralizing enemy combatants and attracting foreign recruits. But the use of beheading is also costly for such groups, notably because of its tendency to alienate potential civilian supporters. Whether or not particular jihadist groups use beheadings depends largely on whether they can afford to ignore these costs. Jihadist insurgents who control significant territory are less sensitive to civilian attitudes because of their ability to obtain support through coercion and are therefore more likely to perpetrate beheadings. The use of beheadings is also shaped by transnational ties: organizations that seek formal affiliation with transnational jihadist networks are more likely to calculate that the benefits of using extreme violence to attract transnational support outweigh its costs. I test this theory using an original dataset of over 1,500 beheading events perpetrated by jihadist organizations between 1998 and 2019.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".