Importance of International Humanitarian Law (IHL) Training in Armed Police Force, Nepal
Bibliographic record
Abstract
International humanitarian law (IHL) applies at times of armed conflict, placing legal obligations on all warring parties that are designed to limit the inhumanity of warfare. Armed Police Force (APF), Nepal with the mandate to control an armed struggle occurred or likely to occur in any part of Nepal, to control armed rebellion or separatist activities or likely to occur in any part of Nepal, and to provide assistance in case of external intervention being under the Nepali Army, can at any time become a party in both international and non-international armed conflict. APF’s role in UN Peacekeeping Missions is also an area where it may have to engage with non-state actors if and when situation demands. All these necessitate APF personnel to have proper understanding and compliance to the principles of IHL, violation of which can increase human suffering and consequent individual criminal responsibility and command responsibility. In light of this, it concludes the IHL specific trainings in APF, Nepal should be maintained and augmented to ensure broad and better understanding and respect for IHL in times of conflict.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".