Challenging the Curious Erasure of Religion from the Study of Religious Terrorism
Bibliographic record
Abstract
Abstract The role that religion plays in the motivation of “religious terrorism” is the subject of much ongoing dispute, even in the case of jihadist groups. Some scholars, for differing reasons, deny that it has any role; others acknowledge the religious character of jihadism in particular, but subtly discount the role of religion, while favoring other explanations for this form of terrorism. Extending an argument begun elsewhere (Dawson 2014, 2017), this article delineates and criticizes the influence of a normative religious bias, on the one hand, and a normative secular bias, on the other hand, on scholarship addressing the relationship between religiosity and terrorism. I examine two illustrative studies to demonstrate the complexity of the conceptual issues at stake: Karen Armstrong’s best-selling book Fields of Blood: Religion and the History of Violence (2014) and a recent article by Bart Schuurman and John G. Horgan on the rationales for terrorist violence in homegrown jihadist groups (2016).
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.040 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.010 |
| 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".