Understanding radicalisation, extremism, and resilience
Bibliographic record
Abstract
This chapter situates the book in the existing scholarship on (de)radicalisation and violent extremism. It traces the origins of the concept of radicalisation in its contemporary use in the context of the war on terror since 2001, and the introduction of the terminology of violent extremism a few years later. It also reviews the evolution of the research field in the last 15 years. It argues that most analysis has focused on individual and psychological dimensions of radicalisation, and there is still little consensus on how and why this process occurs. The chapter then delves into an integrative approach to radicalisation based on the complementarities of different fields and perspectives. As a result, a careful reading of common elements from psychology, identity theories, social psychology, and other fields and theories justifies the need for systemic approaches that also consider social conditions. On the preventive side, resilience has become a common approach and, for many scholars, represents the concept around which an agreement on how to address violent extremism is built. Although the concept and terminology of resilience are also subject to some criticism, the authors argue that a community resilience perspective is best positioned for effective prevention policies and strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".