Bibliographic record
Abstract
The Canons of Judicial Ethics remind me of the old joke about the drunk who's crawling around on all fours under a lamp-post one night.A policeman comes along and asks him his business and the drunk explains that he's looking for a lost quarter.So the policeman offers to help and pretty soon they're both crawling around looking for the coin.After about a half hour of this, the policeman gets fed up and asks: "Are you sure you lost the quarter around here?" "Oh, no," answers the drunk, "I dropped it over in the alley, but it's too dark to look there."So, too, it is with the Canons of Judicial Ethics.The Canons focus on the tensions and potential conflicts that are most easily detected by an outside observer.For example, pretty much everyone agrees that a judge should not sit in judgment on a case on appeal if he participated in the decision below.'Similarly, everyone agrees that a judge may not sit in judgment in a case where he participated as a party or a lawyer.2 Of course, those are just two of the most obvious examples; we have plenty of rules and precedents saying that a judge may not participate in a case where doing so would create the appearance of impropriety.I should mention at the outset that I'm not a fan of this approach to judicial ethics, nor do I believe that it's necessary or inevitable.Take the two examples I've given.As you will recall, in the early days of the Republic the justices rode circuit, and some of the cases they heard in * Judge, United States Court of Appeals for the Ninth Circuit. 1. See MODEL CODE OF JUDICIAL CONDUCT Canon 3(E)(1)(a) (1990).2. See id.Canon 3(E)(1)(b) (former lawyer), Canon 3(E)(l)(d)(i)-(ii) (party and current lawyer).
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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 | 0.021 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.064 |
| Scholarly communication | 0.022 | 0.016 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.014 | 0.023 |
| Insufficient payload (model declined to judge) | 0.007 | 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".