Bibliographic record
Abstract
There can be no issue of greater consequence than the use of force for police and law enforcement professionals. In a transitional post-conflict country like Nepal, internal security agencies play a vital role in managing the complexity of political, social and economic transformation. Ever since the end of the feudal oligarchic militaristic Rana regime, internal security and law enforcement took precedence in providing safety and security for the Nepalese people. Since Nepal entered a democratic transition in 1950, it almost failed to consolidate and strengthen its internal security and law enforcement apparatus by appropriately restructuring and consolidating the Nepal Police and National Intelligence functions. Even after the democratic political transformation of 1950, it took five years to establish the national Nepal Police in 1955. Law enforcement agencies are now challenged to solve problems well beyond traditional scope. Therefore, internal security must come up with highly developed methods of policing and law enforcement. Technologically, this may not be possible yet, but it can provide management and training of its personnel based on world standards of policing and emphasizing knowledge and skill enhancement and professional behavior. This paper has tried to focus and analyze the use of force by internal security apparatus of the Government of Nepal in the past and brain-storm its future roles and challenges. The paper is divided into two parts. The first part will touch upon contextual commentary on the use of force in brief. The second part will highlight three time-tested models and procedure of the use of force.
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 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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.056 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".