Can education counter violent religious extremism?
Bibliographic record
Abstract
s/RésumésViolent religious extremism is a global concern today. As governments prepare their counter-terrorism policies, many focus solely on reactive measures such as military action and surveillance measures – hard power – that are responsive to individuals who are already radicalized. This paper argues that education should be incorporated into such policies as a preventive measure that not only makes students resilient citizens but can also address the psychological, emotional and intellectual appeal of narratives – soft power – that terrorists purport. In doing so, states can counter soft power with the use of soft power in a concerted effort among government departments, social institutions and communities. Our paper clarifies the complexities among fundamentalism, extremism, radicalism and terrorism, and summarizes a variety of push and pull factors that trigger radicalization; it offers as well specific pedagogical recommendations for the Canadian educational system to consider.Aujourd’hui, l’extrémisme religieux violent est une préoccupation à l’échelle internationale. Alors que certains gouvernements préparent leurs politiques antiterroristes, plusieurs autres ne se concentrent que sur des mesures réactives telles que les actions militaires et les mesures de surveillance accrue – hard power – visant particulièrement les personnes qui sont déjà radicalisées. Cet article souligne que l’éducation devrait être intégrée dans ces politiques comme une mesure préventive qui ne rend pas seulement les étudiants citoyens résilients, mais qui peut aussi s’attaquer au discours attrayant sur le plan psychologique, émotionnel et intellectuel – soft power – alimentés par les terroristes. Ce faisant, les États, à travers une action concertée entre les ministères, les institutions et les communautés, peuvent contrer le soft power en utilisant le soft power. Notre article explique les différences complexes entre le fondamentalisme, l’extrémisme, le radicalisme et le terrorisme, et met l’accent sur les différents facteurs qui déclenchent la radicalization. Il propose également des recommandations pédagogiques adaptées au système éducatif canadien.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".