Aspects of Counterterrorism: New Approaches to Countering Terrorism: Designing and Evaluating Counter-Radicalization and De-Radicalization Programs; Hacking ISIS: How to Destroy the Cyber Jihad; Inside Al-Shabaab: The Secret History of Al-Qaeda’s Most Powerful Ally
Bibliographic record
Abstract
Terrorism and the term ‘jihadism’ have become a global phenomenon, a product of modernity and globalization which shows no sign of abating. The number of radicalized young people in Western and non-Western countries who are willing to travel overseas in the cause of jihad and violent extremism has increased significantly since 9/11. In the 20 years since the largely driven U.S. counter-terrorism efforts began in response, jihadism in force and numbers has risen at least by fourfold in terms of the numbers of Sunni jihadist fighters in the field from the Middle East to North Africa, Afghanistan and beyond according to the Center for Strategic and International Studies in 2018 (https:// www.csis.org/analysis/evolution-salafi-jihadist-threat). However we look at it as social scientists, policy makers or interested observers, it represents a failure to some extent of state and society to deal with the global threat of violent extremism at any level, involving any religion, ethnicity or ideological forms which seek to change the political and social order.
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 | 0.030 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".