Erosion of International Organizations’ Legitimacy under Superpower Rivalry: Evidence on the International Court of Justice
Bibliographic record
Abstract
Abstract This article investigates how superpower rivalry affects public perceptions of international organization (IO) legitimacy in the hegemon. We argue that the representation of a superpower rival state at an IO in the form of its key decision maker's nationality can dampen the IO's perceived legitimacy within the rival power. We test this argument using a survey experiment in the United States under President Trump, where we manipulate the nationality of the International Court of Justice (ICJ) judge who casts a tie-breaking vote against the United States. Our results show that when the judge is Chinese, there is a strong and robust dampening of Americans’ perceptions of the ICJ's legitimacy, with no comparable effect arising when the judge is from other countries, including Russia. Replication of the experiment in the United States under President Biden offers external validity for our findings, which may have important implications for the future of the liberal international order.
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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 | 0.013 | 0.088 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".