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Record W4390483455 · doi:10.1007/978-94-6265-619-2_7

Heads of State as War Criminals: The Prospects and Challenges of Tracing War Crimes to Senior Political Leaders in Russia

2024· book-chapter· en· W4390483455 on OpenAlexaff
Frédéric Megret, Camille Marquis Bissonnette

Bibliographic record

VenueYearbook of international humanitarian law · 2024
Typebook-chapter
Languageen
FieldSocial Sciences
TopicTorture, Ethics, and Law
Canadian institutionsUniversité du Québec en OutaouaisMcGill University
Fundersnot available
KeywordsState (computer science)Political sciencePoliticsLawWar crimeCriminologySociologyInternational lawComputer science

Abstract

fetched live from OpenAlex

War crimes are not typically leadership crimes, unlike aggression. In cases where they mobilize important state resources, are committed on a large or consistent scale or are ordered at the highest level, one question is how they can be attributed to heads of state. This chapter discusses some ways in which Vladimir Putin, the current President of Russia, could be brought to trial for alleged war crimes being committed by Russian armed forces in Ukraine. Specifically, it investigates some modes of liability under which could he be held criminally responsible that would particularly make sense of his special responsibilities as head of state. Two main possibilities are discussed: ordering and superior/command responsibility. We evaluate what specific challenges and opportunities each of these options involve. We contend that command or superior responsibility would probably come with the highest chance of success and express a strong sense that the head of state is ultimately responsible for how their troop behave in the field.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0030.002
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.079
GPT teacher head0.342
Teacher spread0.263 · 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 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

Citations1
Published2024
Admission routes1
Has abstractyes

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