The Forms and Ecologies of Islamist militancy and terrorism in Bangladesh
Bibliographic record
Abstract
Islamist militancy and terrorism, a major sociopolitical issue of Bangladesh today, has largely remained under-researched. This paper mainly explores the forms and ecologies of Islamist militancy and terrorism based on the content analysis of media reports and interviews with some experts in Bangladesh. Ecologies, in this paper, refer to the multiple interrelated and interdependent environments: social, political, and religious, that foster, germinate and nurture the growth of militancy and terrorism. Despite Bangladesh being globally identified as a moderate Muslim country located in South Asia, Islamist radicalization, extremism and militancy have become a major concern since the incidence of nationwide serial bomb blasts in 2005. Although an estimation of the group operatives may not be possible because these groups change names or members change groups from time to time, the presence of seventy Islamist militant outfits with thousands of militant members was identified during 1999-2010. Islamist militants carried out over 203 attacks killing 164 innocent people and injuring more than 2,658 people in this period. Using bomb explosions, these attacks targeted political parties, cultural groups, intellectuals, diplomats, movie theatres, NGO offices, and minority religious institutions. In response to these attacks, the government has adopted mostly a law-enforcement centric approach, but this study suggests a comprehensive strategy balancing enforcement, intervention and prevention urgently needed for de-radicalization and counterterrorism in Bangladesh --- the 8th most populous country and the 3rd largest Muslim country of the world. Although foreign journalistic and intelligence-based reports have argued that Islamist militants have links with madrassas (Islamic seminary institutions), this system has actually been in vogue for many years in the country. Research suggests that unemployment problems, poverty, illiteracy, and ignorance about religious matters among the youths are the common drivers for Islamist militancy in Bangladesh. An unfocused national educational policy on madrassas, the narrow and dated madrassa curriculum, and frustrations felt by madrassa graduates who cannot find jobs are likely to instigate the madrassa students to get involved in militancy. Political, educational, and religious interventions are must for countering radicalization effectively. Improving education policy and creating employment opportunities for the unemployed youth can begin to address the problem of Islamist militancy and terrorism in Bangladesh.
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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 | 0.002 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".