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Record W1873505351

Understanding the Behaviour of International Courts: An Examination of Decision-Making at the Ad Hoc International Criminal Tribunals

2010· article· en· W1873505351 on OpenAlexaff
Sébastien Jodoin

Bibliographic record

VenueSSRN Electronic Journal · 2010
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Arbitration and Investment Law
Canadian institutionsMcGill University
Fundersnot available
KeywordsJudicial opinionOrder (exchange)Political scienceCriminal courtLawEmpirical researchPsychologyInternational lawBusinessEpistemology
DOInot available

Abstract

fetched live from OpenAlex

This article seeks to contribute to the literature on the behaviour of international courts (ICs) by focusing on the internal dynamics of decision-making processes within them and by drawing on both strategic and attitudinal models of judicial behaviour developed for domestic courts. The main contention advanced is that the ideas and interests of judges in ICs account for variations in their decision-making. In order to test competing models for understanding the behaviour of ICs, this article includes the first study to examine the decision-making of the ICTR Trial Chambers and of the ICTY and ICTR Appeals Chamber using quantitative methods. The similarities and differences between these ICs have created a rich empirical environment rife with natural experiments. A comparison of decision-making in the ICTY and ICTR Appeals Chambers suggests that the same judges in these two ICs evince stable patterns of judicial behaviour reflecting their attitudinal commitments regarding international law and their conception of the judicial role. A comparison of decision-making between the ICTY and ICTR suggests that judges within these two ICs evince divergent patterns of judicial behaviour in terms of sentencing practices, reflecting dissimilarities in their environments. These results tend to show that both ideas and interests can account for variations in the decision-making of ICs and that while external interests influence judicial decisions, they do so because they have a basis in the ideas and interests of judges.

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)0.010
metaresearch head score (Gemma)0.040
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.040
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0030.008
Scholarly communication0.0060.006
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.031
GPT teacher head0.279
Teacher spread0.249 · 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 designObservational
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

Citations3
Published2010
Admission routes1
Has abstractyes

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