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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".