Selecting candidates to the bench of the World Court: (Inevitable) politicization and its consequences
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it