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Record W2303507933 · doi:10.1093/jicj/mqv053

Sandesh Sivakumaran,<i>The Law of Non-International Armed Conflict</i>

2015· article· en· W2303507933 on OpenAlexaff
Camille Marquis Bissonnette

Bibliographic record

VenueJournal of International Criminal Justice · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicAsian Geopolitics and Ethnography
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsInternational humanitarian lawLawInternational lawPolitical scienceMunicipal lawArmed conflictComparative lawPublic international lawHuman rights

Abstract

fetched live from OpenAlex

The most common type of contemporary armed conflict is fought between a state and one or more armed groups, or between two or more armed groups, yet the law regulating non-international armed conflicts has been barely addressed by academics in comparison to the law on international armed conflicts. Moreover, in general, this field of law is analysed on a subsidiary basis, from the point of view of its divergences and gaps as to the latter, rather than systematically and independently. Sandesh Sivakumaran’s book is unique as it focuses solely on the law — understood broadly, not merely as comprising international humanitarian law — applicable to non-international armed conflicts and presents the way such conflicts are regulated as well as the specific rules that constitute this body of law. It thus treats the law of non-international armed conflicts as a whole, including international humanitarian law, international criminal law and international human rights law, which have been traditionally analysed separately. In addition, Sivakumaran clarifies — by adopting a historical perspective on various specific aspects of the regulation of armed conflicts throughout the book — the changes in perspectives and contributions brought by each of these bodies of law, including, but not limited to, humanitarian law applicable to international armed conflicts.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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: none
Teacher disagreement score0.729
Threshold uncertainty score0.336

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.061
GPT teacher head0.374
Teacher spread0.313 · 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 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
Published2015
Admission routes1
Has abstractyes

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