Bibliographic record
Abstract
A colleague later reminisced that Judge Jones' "beautifullv crafted opinions stand out in the New York Reports as models of scholarship, clarity of thought, and lucid gracefil wordsmanship.""The opinions were "clear, crisp, powerful writings," ChiefJudge Judith S. Kaye certified, "not a spare or careless word in them."'I began my career as one of JudgeJones' law clerks more than 40 years ago.This article shares the judge's enduring lessons about the constructive role that editing from law clerks, and sometimes from other inner court staff, can play in preparing an opinion's preliminary drafts.These lessons, taught during my two-year appointment, can influence riot only judges who have access to these human resources, but also other legal writers who value constructive editing from colleagues in their law v offices.In opinion writing, a judge's editorial process balances three forces that this article explores below.The firstethical constraints expressed in the Model (ode of judicial Conduct 1 unique to judging.But the second and third forces are comion to all legal writing pride of authorship (the writer's emotional attachment to what the writer has already put on paper); and personal and professional modesty (the writer's capacity to restrain this pride and carefully weigh editors' input during a writing's gestation, 6/21/18
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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 | 0.014 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.018 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.043 | 0.026 |
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".