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Record W2900244530 · doi:10.1080/03069400.2018.1526472

Legal Education and Training Review: a five-year retro/prospective

2018· article· en· W2900244530 on OpenAlexaff
Jane Ching, Paul Maharg, Avrom Sherr, Julian Webb

Bibliographic record

VenueThe Law Teacher · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Education and Practice Innovations
Canadian institutionsYork University
Fundersnot available
KeywordsCompetence (human resources)CommissionPolitical scienceLegal educationPublic relationsSpace (punctuation)Legal serviceEngineering ethicsPublic administrationLawPsychologySocial psychologyEngineeringComputer science

Abstract

fetched live from OpenAlex

The Legal Education and Training Review final report on the regulation of legal services education and training was published in June 2013. Five years later, members of the research team reflect, in this article, on subsequent developments in the relationship between regulator and regulated. They explore the links between outcomes-focused regulation (OFR) and the hierarchies within the regulatory space and between the OFR-driven focus on competence and its impacts on assessment for qualification and continuing competence thereafter. Finally, they extend the concept of shared space to include the relationship between regulators who commission research and researchers who carry it out. The paper concludes that the project has attracted international interest and informed other projects. Although there is already clear impact in England and Wales, the full significance of the report in the canon of seminal reports into legal education will emerge over the next decade.

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)0.110
metaresearch head score (Gemma)0.128
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.110
Threshold uncertainty score0.582

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1100.128
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0240.016
Science and technology studies0.0030.002
Scholarly communication0.0110.006
Open science0.0030.007
Research integrity0.0090.005
Insufficient payload (model declined to judge)0.0060.004

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 head0.045
GPT teacher head0.394
Teacher spread0.349 · 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 designQualitative
Domainnot available
GenreReview

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

Citations3
Published2018
Admission routes1
Has abstractyes

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