Clarifying the Explanatory Context for Developing Theories of Radicalization: Five Basic Considerations
Bibliographic record
Abstract
We know a great deal more about the process of radicalization leading to violence than when the term entered the popular lexicon a few years after 9/11. Yet fundamentally, it remains difficult to specify who will turn to political violence, how, or why. Progress on this key issue depends on many developments. This article reviews and analyses five basic meta-methodological insights, on which there is growing consensus, which set the parameters for the ongoing study and modeling of radicalization: (1) the specificity problem; (2) the shift from profiles to process; (3) the necessity of a multi-factorial approach; (4) the heterogeneity problem; and (5) the primary data problem. The objective is to create a stronger understanding of the nature and collective relevance of these accepted insights, and point to two related emergent issues on which more systematic research still needs to be done in the context of combatting terrorism: the relationship of attitudes and behavior, and the problem of accounts (i.e., the critical and contextual study of how people justify or excuse socially undesirable or problematic behavior and occurrences).
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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 | 0.160 | 0.200 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.005 | 0.027 |
| Scholarly communication | 0.015 | 0.041 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.006 | 0.015 |
| Insufficient payload (model declined to judge) | 0.008 | 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".