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Record W4396887566 · doi:10.29173/mlj933

Modern Legal Education: Towards Practice-Ready Attitudes, Attributes and Professionalism

2016· article· en· W4396887566 on OpenAlexaboutno aff
Jonathan L. Black-Branch

Bibliographic record

VenueManitoba Law Journal · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Education and Practice Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsLegal educationEngineering ethicsPolitical scienceSociologyPsychologyMathematics educationLawEngineering

Abstract

fetched live from OpenAlex

egal education today is undergoing unprecedented changes across Canada and around the world amidst an ever-emerging global digital economy.Law societies, national federations, and bar associations are examining curriculum and skills-sets required to prepare lawyers for modern practice; many recommending new approaches to the delivery of legal education and introducing core competency profiles for admission to practice law.Legal knowledge, expertise, and professional skills are under immense scrutiny focusing on what law schools should provide and what law associations expect of their members.Some jurisdictions are exploring alternative pathways to licensing post-law school at a time when competition amongst students for legal positions remains intense.The purpose of this article is to provide a brief synopsis as to how I see the future of legal education, exploring the aim and focus of a modern law school.I argue that legal education needs reform with an eye to preparing students for practice by developing appropriate research skills and providing clinical opportunities in which students can apply their knowledge.Teaching reform and innovation needs to focus on balancing subject content knowledge with an emphasis on clinical learning opportunities and practice-based experiences supervised by practising lawyers and judges.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.946
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0010.004
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.081
GPT teacher head0.416
Teacher spread0.334 · 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.

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
Published2016
Admission routes1
Has abstractyes

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