All Rise: The Prospects and Challenges of Lower Federal Judicial Biography
Bibliographic record
Abstract
Charles Zelden’s essay, “All Rise: The Prospects and Challenges of Lower Federal Judicial Biography,” picks up where Kobrick concluded. Zelden laments the paucity of biographies of lower-federal-court judges, while nonetheless 12. See Funk, infra ch. 2. 13. See Hall, infra ch. 3. 14. See Grisinger, infra ch. 4. 15. See Kobrick, infra ch. 5. 5 Approaches to Federal Judicial History Federal Judicial Center appreciating the challenges that routinely face these biographers. Historically speaking, Zelden writes, lower federal judges “are generally not well known, the importance of their work is not self-evident, their papers are often scattered or fragmentary or thin, and the wider context in which they operate is not wellestablished.” But especially because the lower federal courts are the front line of interaction between the federal judiciary and the people, Zelden believes that biographers should persist in trying to write more and better biographies of lower federal judges. This is a unique opportunity, he concludes, to weigh “the difference between law on the books and law as applied” throughout the federal judiciary.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.023 | 0.017 |
| Scholarly communication | 0.021 | 0.015 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".