Caught between ‘Crossfire’ in the Context of Bangladesh
Bibliographic record
Abstract
In recent times, the law enforcement agencies of Bangladesh are universally appreciated for their constitutive and plucky attitude to extremist gangs inside the country. Contrariwise, a suspicious incident of a particular form of extrajudicial killing; Crossfire is fading their achievements. Initially, it was a media term, but now widely used to express the murder of a criminal or accused in a gunfight event between members of law enforcement agencies and criminal groups. This occurrence is facing enormous criticisms in the home and abroad and considered as a violation of human rights. Though public notions about these incidents are surprisingly flexible and they consider this for a prognosis to remainder culprits. This paper analyzed the justice idea of both groups; who are for and against this event from a moral philosophical perspective in the context of Bangladesh. Both the utilitarian idea analyzed by Jeremy Bentham (consequences) and John Stuart Mill (individual human rights) echoes the voice of these two distinct groups respectively. However, the article advocates for a distinctive idea of justice known as deontological philosophy proposed by Immanuel Kant. This moral ideology concentrates on universal human rights and keeps the consequences aside. Considering the fact ‘Crossfire’, this paper believed there is no alternative to ensuring justice and enacting moral duty of law enforcement agencies to indemnify security and safety of the citizen of Bangladesh.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".