Perceptions of Justice- An International Perspective on Judges and Apperances
Bibliographic record
Abstract
Responsibility, 89JUDICATURE 35, 35 (2005) ("To hold judges to the highest standard of ethical conduct, a code of judicial conduct must cover not just the clear and obvious improprieties but indirect, disguised, or careless conduct that looks like an impropriety to an observer who is neither overly suspicious nor unusually gullible.");Nancy J. Moore, Is the Appearance of Impropriety an Appropriate Standard for DiscipliningJudges in theTwenty-First Century?, 41 LoY.U. CHI.L.J. 285, 291 (2010) [hereinafter Moore, Appearance of Impropriety] ("Avoiding not only impropriety, but also the appearance of impropriety, is important for judges because public confidence in the independence, integrity, and impartiality of the judiciary is critical to the public's willingness to accept judicial decision-making and submit to the rule of law.");Randall T. Shepard, Campaign Speech: Restraint and Liberty in Judicial Ethics, 9 GEO.J. LEGAL ETHICS 1059, 1067 (1996) ("U]udges must not only be impartial in fact, they must appear to be impartial.");see also UNITED NATIONS OFFICE ON DRUGS & CRIME, COMMENTARY ON THE BANGALORE PRINCIPLES OFJUDICIAL CONDUCT 17 (2007) [hereinafter Commentary on the Bangalore Principles] ("J]udges must not only meet objective criteria of impartiality but137 2013] § 1.1, at 5. 9. See Dr. Bonham's Case, 77 Eng.Rep. 638 (K.B.), 8 Co. Rep. 113b, 1 18a; see also John P. Frank, Disqualification offudges: In Support of the Bayh Bill 35 LAW & CONTEMP.PROBS.
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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 | 0.016 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.032 |
| Scholarly communication | 0.021 | 0.009 |
| Open science | 0.001 | 0.005 |
| 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".