Religious Redemption as the Motivation for the Jihadist Crime-Terrorism Nexus: A Critical Inquiry
Bibliographic record
Abstract
In their influential article on the “crime-terror nexus” Basra and Neumann state that “jihadism can affect a criminal’s radicalisation process in two ways: it can offer redemption from past sins, or it can legitimise crime.” During their analysis, though, the two interpretive options become in effect one, reflecting the dominant orientation to the continuity of criminality and terrorism (religious or otherwise) as social phenomena. Examining the work of Basra and Neumann, and others addressing the issue, this article argues for a crucial aspect of discontinuity between some jihadists’ terrorist commitments and their criminal pasts. The redemptive motivation for turning from criminality to jihadism warrants being analyzed more fully and carefully to better explain why only a handful of individuals with a criminal background become jihadists. Crucially, for example, and contrary to a prevailing narrative in the literature, jihadists appear to prioritize seeking redemption for their sins, as defined by their religion, rather than for crimes, as delineated by secular society. Fully recognizing and investigating the “definition of the situation” that Western Muslim criminals and jihadist recruiters share is essential to understanding the motivations for the nexus in many instances, and thus grasping how best to counter it.
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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 | 0.010 | 0.009 |
| 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 | 0.081 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".