Beyond community policing: Human security-informed approach to community security
Bibliographic record
Abstract
This paper examines the effectiveness of community policing in addressing comprehensive security needs through a human security-oriented lens. While traditional policing emphasizes law enforcement and crime prevention, increasing societal complexity and recurring civil unrest expose significant gaps in how broader security concerns are managed. The study investigates whether community policing genuinely enhances overall security and explores how policing strategies can better promote safety and trust within communities. Using empirical data from a recent study in Nepal and a comparative analysis of community policing models in the United States, the United Kingdom, Canada, and Denmark, the research identifies key factors shaping insecurity beyond crime—such as intimidation, lack of justice access, poor income conditions, and inadequate health services. Although community policing has improved police–community relationships and public trust, it often fails to respond effectively to localized, context-specific security challenges. Perceptions of security differ widely depending on environment, gender, age, and ethnicity, underscoring the need for a more inclusive approach. The findings argue for a human security-informed model of policing that integrates socioeconomic measures—like employment, education, and access to basic services—with enhanced police practices emphasizing integrity, visibility, investigative capacity, and community partnership. This holistic framework bridges the existing gap between conventional policing and broader human security concerns. By addressing the social and economic dimensions of insecurity, such an approach strengthens both trust and safety. The paper highlights its particular relevance for post-conflict societies like Nepal and other nations prioritizing the security development nexus.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.009 | 0.023 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".