The Plasticity of the Islamic Activist: Notes from the Counterterrorism Literature
Bibliographic record
Abstract
Western intelligence analysts fight an uphill battle to avoid parochial habits of thought that lump diverse Islamist identities together. The recent counterterrorism literature gives us tools for understanding a wide spectrum of Islamists, by focusing attention on what they say about themselves rather than on the intelligence labels we must ultimately assign to them. The main challenge for analysts is not the brute diversity of Islamist types, but their plasticity—the Islamist's flexible inhabitation of distinct, sometimes contradictory, identities. Contrary to generalizations about the duplicity of all Islamists, much plasticity is due to ordinary psychological- or ideological strain—the inability to resolve divided allegiances or sustain conflicted principles. Islamist preacher Yussef al-Qardawi and salafist group Hizb ut-Tahrir present prime examples of ordinary Islamist plasticity. In order to understand Islamism in all its complexity, analysts should develop methods for disaggregating and evaluating key components of the Islamist persona.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.015 | 0.041 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.008 |
| 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".