The Growing Complexity of the International Court of Justice’s Self-Citation Network
Bibliographic record
Abstract
Using state-of-the-art information extraction, this article identifies 1,865 references in judgments of the International Court of Justice (ICJ) between 1948 and 2013 to its own decisions or those of its predecessor. We find that the ICJ’s self-citation network becomes increasingly complex. Citations are used more frequently, and precedents grow more diverse. Two drivers fuel this development. First, subject matter concentration clusters citations in ‘classic’ international law areas as the ICJ places increased emphasis on the legacy, expertise and predictability of its ‘settled jurisprudence’ in asserting its role among competing adjudicatory venues. Second, issue diversification expands citations as disputants increasingly craft their arguments around precedent, making ICJ litigation more common law-like. This translates into more complex litigation as precedent is predominantly used argumentatively to affect outcomes rather than ritualistically to pay tribute to past decisions. Although the growth of citations is an institutional achievement underscoring the Court’s continued relevance, it also creates new access-to-justice barriers.
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 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 | 0.013 | 0.138 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.085 | 0.113 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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.
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".