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Record W2108094981 · doi:10.1093/jcsl/krt026

Jus ad bellum and American Targeted Use of Force to Fight Terrorism Around the World

2014· article· en· W2108094981 on OpenAlexaff
Anders Henriksen

Bibliographic record

VenueJournal of Conflict and Security Law · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Law and Human Rights
Canadian institutionsInstitute on Governance
Fundersnot available
KeywordsJus ad bellumTerrorismPolitical scienceLawSelf defenseUse of forceInternational lawCriminologySociology

Abstract

fetched live from OpenAlex

In recent years the USA has conducted hundreds of targeted operations against alleged terrorists in Pakistan, Yemen, Somalia and Libya, and the article analyses if the operations comply with jus ad bellum. The first part of the article summarizes the status of the relevant legal principles and concludes that the right to self-defence has undergone substantial changes since the attacks on 9/11. The second part analyses if the American operations comply with the legal framework just presented and conclude that the majority of the operations appear to be based on local consent and therefore comply with the jus ad bellum. The operations may also be lawful if they comply with a right to self-defence, but it is submitted that the attacks on 11 September 2001 can no longer serve as a basis for an American right to self-defence. To justify its operations as self-defence, the USA must therefore point to other armed attacks. It must also show that the local authorities are unwilling or unable to stop the attacks from their territories. It is concluded that the attacks on American personnel in Afghanistan by groups in the tribal areas of Pakistan constitute an armed attack on the USA, and that the Americans were also the victim of a 2010 armed attack from AQAP in Yemen. The USA has not, however, suffered an armed attack from the hands of al Shabaab in Somalia or from anyone in Libya that can justify the capture of a Libyan citizen in October 2013.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.946
Threshold uncertainty score0.720

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.021
GPT teacher head0.295
Teacher spread0.274 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations33
Published2014
Admission routes1
Has abstractyes

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