Community policing in preventing Violent Extremism and Radicalization that Lead to Terrorism in Bangladesh
Bibliographic record
Abstract
Abstract Efforts to prevent Violent Extremism and Radicalization that Lead to Terrorism (VERLT) are fundamentally unsustainable without trust-based partnerships and community inclusion. The article examines the current state, potential, and challenges of community policing (CP) as a form of police volunteerism aimed at preventing VERLT in communities. To explore the grassroots realities of CP initiatives, we gathered qualitative data from three regions of Bangladesh. Our data revealed that despite its functional limitations, CP has emerged as a catalyst in fostering police-public trust, reducing fear and stigma associated with the police, raising awareness, and cultivating a belief in society's collective ability to prevent VERLT. All stakeholders of CP recognize and value the significance of involving women volunteers; nonetheless, the current state of practice is heavily gendered. The article identifies important culturally embedded concerns and political barriers to adopting CP in Muslim-majority countries, to inform appropriate, context-specific policy responses. Finally, the article argues that the conventional, well-known community policing models used in the West may not be effective in Bangladesh. The outlined challenging factors call for an indigenous, decolonial, tailored, community-driven approach that is in respectful harmony with local socio-cultural traditions and expectations.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".