Are returning foreign fighters dangerous? Re-investigating Hegghammer’s assessment of the impact of veteran foreign fighters on the operational effectiveness of domestic terrorism in the West.
Bibliographic record
Abstract
This paper looks at the impact of returning foreign fighters on terrorism plot effectiveness in the West by using a modified version of Hegghammer's Jihadi Plots in the West (JPW) datasets. Counter-foreign fighter policies are designed on the assumption that returning foreign fighters increase the operational effectiveness of terrorist cells and plots. Previous research on foreign fighters have provided ambiguous conclusions on the impact of their return on terrorism in the West. In particular, researchers point to a low probability, high impact scenario where veteran foreign fighters increase the number of casualties for a given terrorist plot. This paper re-investigates these conclusions by using an updated data set. It makes use of logistic and multiple regression analysis using the difference-to-difference approach. It finds that the presence of veteran foreign fighters in terrorist cells do not increase the chances that the plot will be executed and that if the plot is executed, the presence of veteran foreign fighters has no impact on the number of casualties. In the conclusion, it argues that counter-foreign fighter policies are thus designed to prevent a very low threat. In doing so, they create potential issues by preventing the demobilization of foreign fighters. Instead, counter-foreign fighter policies should focus on reintegration in order to utilize foreign fighters to improve intelligence-gathering capabilities and create better de-radicalization programs.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".