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Record W3155081451

The Nuremberg and Tokyo Trials Legacy

2018· article· en· W3155081451 on OpenAlexaff
Miriam Cohen

Bibliographic record

VenueSSRN Electronic Journal · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Law and Human Rights
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsNuremberg trialsHuman rightsAccountabilityPolitical scienceMilestoneWar crimeLawPunishment (psychology)International lawCriminologySociologyPsychologyHistory
DOInot available

Abstract

fetched live from OpenAlex

The trials in Nuremberg and Tokyo in the aftermath of the Second World War represented a milestone in international law and human rights. While mass atrocities were committed during the War, a system of human rights protection had not yet been conceived. In the aftermath of the War, the concept of accountability took the shape of criminal prosecutions of international crimes that were committed during the War. The trials focused on violations of human rights that amounted to international crimes. The Nuremberg trials marked the beginning of an era where mass atrocities are accounted for. This chapter dwells upon the development of the Nuremberg and Tokyo trials, including their legacy. It then addresses one major gap in their trials: albeit a major historical step forward in accountability, the Nuremberg and Tokyo trials focused on the offenders and their punishment and thus left a gap in relation to reparations for victims. Human rights institutions were later formed to fill in this gap, from a State responsibility perspective.

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)0.006
metaresearch head score (Gemma)0.011
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: none
Teacher disagreement score0.030
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0070.019
Scholarly communication0.0090.009
Open science0.0010.008
Research integrity0.0040.008
Insufficient payload (model declined to judge)0.0140.002

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 head0.025
GPT teacher head0.346
Teacher spread0.321 · 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
Published2018
Admission routes1
Has abstractyes

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