Homegrown terrorism and transformative learning: an interdisciplinary approach to understanding radicalization
Bibliographic record
Abstract
Since 2001, a preponderance of terrorist activity in Europe, North America, and Australia, has involved radicalized Westerners inspired by al Qaeda. Described as ‘homegrown terrorism’, perpetrators are citizens and residents born, raised, and educated within the countries they attack. While most scholars and policy-makers agree that radicalization plays a central role in persuading Westerners to embrace terrorism, little research properly investigates the internal and cognitive processes inherent to radicalization. Transformative learning theory, developed from the sciences in education, health, and rehabilitation, provides an unconventional and interdisciplinary way to understand the radicalization process. The theory suggests that sustained behavioural change can occur when critical reflection and the development of novel personal belief systems are provoked by specific triggering factors. In applying transformative learning theory to homegrown terrorism, this study helps explain how formerly non-violent individuals come to condone, legitimize, and participate in violent behaviour.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.057 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".