Bibliographic record
Abstract
Around the world, traveling judges sit on domestic courts outside their home jurisdictions. They are hired as trusted outsiders to promote the hiring court as a hub for commercial law, to maintain ties between legal systems, and to aid rebuilding and regime transition. Along with the more familiar dilemmas that all judges can face, traveling judges face ethical concerns tied to their frequently episodic and short-term roles. Those invited to join courts as traveling judges also face questions about whether to accept a position in the first place. These concerns have not been examined in a systematic way. Judges and courts are reliant on individual senses of integrity and, ultimately, on the willingness of these traveling judges to resign. This article proposes that traveling judges should be viewed as trusted outsiders and argues for the development of specific rules attaching to their role as well as standards for accepting and continuing in a job. In particular, it proposes common transnational soft law rules around issues like conflicts of interest, renewability of terms, and work visas. Adopting such rules is a necessary, but not sufficient, condition for taking and continuing in a specific job. I also propose some further questions that judges should ask before they agree to work.
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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.032 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".