Bibliographic record
Abstract
Technology plays an incontrovertibly central role in contemporary judicial work and lives, both on and off the bench. Along with tremendous benefits, it imports substantial new challenges that increasingly impact upon courts and judicial ethics. And yet, notwithstanding its growing relevance, the question of technology's ramifications for the judiciary has thus far evaded scholarly inquiry almost entirely, leaving courts (for the most part) with little choice but to attempt to fit new technologies into outdates regimes and practices. Online court records and privacy, ex parte email communication (by self-represented litigants), inadvertently e-mailed draft decisions and the matter of independence and government-owned and operated court servers are but a few of the plentiful issues arising with greater - indeed disconcerting - frequency. The cumulative effect of these, it stands to reason, is to ultimately prompt courts to revisit the conventional construction of fundamental concepts including disclosure, competence - even impartiality - and the balance to be struck between foundational values such as transparency and privacy in the Internet age. In an effort to alert judges to up-and-coming matters deriving from the use of technology, the following will first endeavor to highlight issues arising from the interplay between technology and judging. It will then more specifically address two of the referenced issues namely, the networked environment's ramifications for out-of-court judicial expression and judicial use of online resources (including search engines and Wikipedia) as it relates to competence and diligence, inter alia.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".