International Courts’ Shadow Effects and the Aims of Judicialized International Cooperation
Bibliographic record
Abstract
In “Judicialization of the Sea: Bargaining in the Shadow of UNCLOS,” Sara Mitchell and Andrew Owsiak make a valuable contribution to an expanding body of scholarship that considers whether and how international courts have out-of-court “shadow effects.” The authors argue that, in the UN Convention on the Law of the Sea (UNCLOS) regime, the threat of binding international dispute settlement (IDS)—which entails high costs for states—encourages rational potential litigants to settle out of court through other peaceful and less costly IDS mechanisms. In this essay, I challenge the narrow focus of Mitchell and Owsiak's analysis, considering the diverse aims and processes of judicialized international cooperation in two key ways. First, the authors’ focus on peaceful IDS as the sole outcome of interest overlooks other important cooperation goals driving judicialization and delegation to international courts. An emphasis on out-of-court IDS, even when achieved peacefully, can actually undermine other objectives for judicialized international cooperation, including the development of international law and greater compliance with international law. Second, Mitchell and Oswiak's theoretical mechanism assumes that an international court contributes to its out-of-court influence through its case law, but this discounts how international courts can engage in a range of out-of-court, non-adjudicative activities that can affect potential litigants’ cost-benefit analyses regarding judicialized versus non-judicialized IDS. Indicating its preference for increasing its “direct effects” through adjudicating disputes, the International Tribunal for the Law of the Sea (ITLOS) has developed capacity-building and training programs to encourage judicialized IDS under UNCLOS and states’ litigation at the ITLOS. Overall, I highlight how there is a broad range of actors and processes underpinning international courts’ out-of-court effects, and how these actors and processes can work towards multiple, at times conflicting, aims for judicialized international cooperation.
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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.027 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".