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Record W3122809876

Seduction of a Law Professor

2013· article· en· W3122809876 on OpenAlexaff
Shauna Van Praagh

Bibliographic record

VenueSSRN Electronic Journal · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicLegal principles and applications
Canadian institutionsMcGill University
Fundersnot available
KeywordsLawSelection (genetic algorithm)Task (project management)Political scienceSociologyPhilosophyComputer scienceManagementEconomicsArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

This review of Allan Hutchinson’s book on ‘Great Judges’ is an engaged reflection on two of the book’s central purposes. Hutchinson’s book reflects a double-pronged preoccupation: first, a preoccupation with “choosing” individuals who satisfy the criteria Hutchinson offers for being included on a shortlist of “great judges”, and second, a preoccupation with “musing” as to the contributions of these selected few to the creation and continuity of the common law. This “choosing and musing” project takes very seriously the task of selecting great judges and justifying that selection.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.012
Scholarly communication0.0060.007
Open science0.0020.004
Research integrity0.0050.009
Insufficient payload (model declined to judge)0.0180.008

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 head0.015
GPT teacher head0.307
Teacher spread0.292 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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
Published2013
Admission routes1
Has abstractyes

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