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Record W32134077 · doi:10.29173/alr38

Legal Education Reform and the Good Lawyer

2014· article· en· W32134077 on OpenAlexaffvenue
Alice Woolley

Bibliographic record

VenueAlberta Law Review · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Education and Practice Innovations
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsLegal educationDoctrineLegal doctrineLawEmphasis (telecommunications)Function (biology)Legal ethicsLegal professionIdentity (music)Political scienceLegal practicePractice of lawLegal writingLegal researchLegal realismProfessional responsibilitySociologyPsychologyEngineeringPhilosophy

Abstract

fetched live from OpenAlex

The critics agree: law schools do it wrong. Stuck in early twentieth century practices that emphasize instruction in legal doctrine in large lecture halls, law schools fail to provide their students with the skills necessary to be practicing lawyers and to be marketable to prospective employers. They fail to instill in their students the “professional identity” necessary to achieve ethical legal practice. This article sounds a cautionary note with respect to those proposals for reform that reject the traditional emphasis on doctrinal teaching. In particular, and in contrast to the critics who view doctrinal learning as inconsistent with, or unrelated to, the creation of ethical lawyers, this article suggests that the emphasis on law in law school serves an essential function in creating ethical legal practice.

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)0.013
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.074
Threshold uncertainty score0.186

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.032
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0060.031
Scholarly communication0.0110.007
Open science0.0020.003
Research integrity0.0150.013
Insufficient payload (model declined to judge)0.0080.001

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 head0.020
GPT teacher head0.361
Teacher spread0.341 · 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 designTheoretical or conceptual
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
Published2014
Admission routes2
Has abstractyes

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