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Record W6980351152

Building on Strong Foundations: Rethinking Legal\nEducation with a View to Improving Curricular Quality

2006· article· en· W6980351152 on OpenAlexaboutno aff

Bibliographic record

VenueeYLS (Yale Law School) · 2006
Typearticle
Languageen
FieldEngineering
TopicThermodynamic and Structural Properties of Metals and Alloys
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumFoundation (evidence)Legal educationQuality (philosophy)Economic JusticeComprehensionLegal professionPractice of law
DOInot available

Abstract

fetched live from OpenAlex

Recent increases in law school tuition provide an occasion for criticalreflection on precisely what law students are being offered in their formal education. The aim of this article is to help catalyze discussion of what quality legal education entails. It begins by outlining the current underpinnings of Canadian legal education, especially the foundation of issue identification. Newer developments in legal education are also canvassed.A foundational critique is then applied to elucidate the main weakness of thepresent curricular structure: students are graduating with a flat understanding of the law Employing Dr Oliver Sacks's critique of medical education as a starting point, the author proposes a vision of a re-invigorated legal curriculum built on twin foundations of identification and understanding. It is suggested that legal educators might, in practice, build on the foundation of understanding by addressing four key areas in which the traditional curriculum shows weakness: client understanding; comprehension of the law in a broader context; understanding of the actors and institutions of the justice system; and self-knowledge. Practical teaching tips are offered to encourage the formation of more well-trained, well-rounded graduates better able to serve society upon graduation.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.528
Threshold uncertainty score0.693

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.013
GPT teacher head0.250
Teacher spread0.236 · 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 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
Published2006
Admission routes1
Has abstractyes

Explore more

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