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Record W3083806211 · doi:10.1017/9781108769105.015

Jean Graven

2020· book-chapter· en· W3083806211 on OpenAlexaff
Romane Laguel, Damien Scalia

Bibliographic record

VenueCambridge University Press eBooks · 2020
Typebook-chapter
Languageen
FieldArts and Humanities
TopicHistorical and Contemporary Political Dynamics
Canadian institutionsMcGill University
Fundersnot available
KeywordsLawCriminal lawPolitical scienceInternational lawHumanityDiplomacyInterrogationAssertionCrimes against humanityCriminal codeInternational humanitarian lawLeagueIdeal (ethics)TerrorismPoliticsWar crime

Abstract

fetched live from OpenAlex

“We should entrust criminal law with the defense of international peace and universal order, a task neither diplomacy nor the politics of the League of Nations were able to carry.” This was the view of the former professor of criminal law at the University of Geneva, Jean Graven, after he attended the Nuremberg Trial. Originally a criminal lawyer, he supported the idea of international criminal law through its dissemination and teaching from 1948 onwards. Among good examples of his efforts are his course on crimes against humanity before The Hague Academy; a course of international criminal law he taught in Geneva but also in Teheran and Cairo; and his draft of the Ethiopian criminal code in which he tried to implement international crimes. As a broker of the idea of an ideal international criminal law, Jean Graven did not address the criticisms levelled against Nuremberg. He rather stood firm by its fundamental idea: the fight for a common concern of humankind. The unpublished documents in his personal library support this assertion.

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)0.000
metaresearch head score (Gemma)0.001
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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.106
Threshold uncertainty score0.353

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0040.003
Open science0.0000.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.1060.042

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 head0.042
GPT teacher head0.172
Teacher spread0.130 · 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
GenreOther

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
Published2020
Admission routes1
Has abstractyes

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