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Record W2586674664 · doi:10.5539/mas.v11n2p87

Analysis of Criminal Responsibility of Users of Chemical Weapons in International Documents

2017· article· en· W2586674664 on OpenAlexvenueno aff
Mahmud Buolagh, Habib Zuori

Bibliographic record

VenueModern Applied Science · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Law and Human Rights
Canadian institutionsnot available
Fundersnot available
KeywordsInternational humanitarian lawChemical warfareLawInternational lawPolitical scienceCriminal lawJurisprudenceNuclear weaponProscriptionWar crimeCriminal responsibilityPolitics

Abstract

fetched live from OpenAlex

The article is written entitled "Analysis of criminal responsibility of the users of chemical weapons". The issue of chemical weapons and criminal responsibility of users of this weapon is very important because countries like Iran starting a war of aggression and invasion against violations of humanitarian law, international criminal is not considered responsibility for the instigators of war. This study aimed to explore international responsibility of individuals and legal assign and use of chemical weapons and the role of domestic law in support of victims of such weapons has been developed. The main hypothesis of this study tries to answer this question that what challenges are dominant criminal responsibilities of users of chemical weapons. It states that; the impact of global powers on international issues are the lack of law, guarantees, good performances and the most important challenges is the lack of cooperation by governments. This research, descriptive analysis of documents, according to primary sources, including data and legal jurisprudence and secondary sources, including laws papers has been developed. The findings of this study show that, the principles of international law banning the use of chemical weapons, in addition to the contract law, is customary international law, hence, even if the country has no weapons of mass destruction also a member of the international conventions, proscription, in case of violations of international humanitarian law can still be prosecuted in the s.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.028
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0110.011
Science and technology studies0.0020.002
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.034
GPT teacher head0.365
Teacher spread0.332 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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