Selecting candidates to the bench of the World Court: (Inevitable) politicization and its consequences
Bibliographic record
Abstract
Abstract Judges of the International Court of Justice (ICJ) are prominent jurists of high merit. However, little is known about certain extra-legal factors of the candidates that guide states in their selection and appointment process. This article focuses on examining extra-legal factors that matter for states in the selection process. Such extra-legal factors demonstrate that elections of candidates to the Court constitute another aspect of a broader political struggle to define the meaning of international law. The article situates the discussion on the selection process in the broader context of the discussion on biases in international law to suggest that the election of candidates to the Court becomes both an instrument and a procedure for controlling the discourse. The characteristics of the judges thus matter as a proxy to control the production and direction of such discourse. This article then explores the ways in which some states have greater strategic advantage in the selection and election processes that enables them to control the discourse to define the meaning of international law effectively.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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.
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".