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Record W6979837083

All Rise: The Prospects and Challenges of Lower Federal Judicial Biography

2020· other· en· W6979837083 on OpenAlexfundno aff

Bibliographic record

VenueNSUWorks (Nova Southeastern University) · 2020
Typeother
Languageen
FieldMedicine
TopicFoot and Ankle Surgery
Canadian institutionsnot available
FundersUniversity of Illinois at Urbana-ChampaignUniversity of OxfordJohns Hopkins UniversityFordham UniversityLouisiana State UniversityUniversity of CambridgeYale UniversityBrandeis UniversityHarvard UniversityYork UniversityUniversity of PennsylvaniaAustralian GovernmentPrinceton University
KeywordsContext (archaeology)Federal lawJudicial reviewBiographyFederalismWork (physics)Federal court
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.673
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.034
GPT teacher head0.221
Teacher spread0.187 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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