Understanding the Behaviour of International Courts: An Examination of Decision-Making at the Ad Hoc International Criminal Tribunals
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".