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Record W66244103 · doi:10.60082/0829-3929.1196

Multi-disciplinary Practice in a Community Law Environment: Clinical Legal Education Combined with Holistic Service Provision

2014· article· en· W66244103 on OpenAlexvenueno aff
Richard W. Foster

Bibliographic record

VenueJournal of Law and Social Policy · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Education and Practice Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsDisciplineLeasehold estateService (business)BankruptcyLegal educationService-learningPractice of lawNeighbourhood (mathematics)Legal researchEngineering ethicsPublic relationsLawSociologyPolitical scienceLegal professionEngineeringBusinessMarketing

Abstract

fetched live from OpenAlex

The Monash-Oakleigh Legal Service (MOLS) is a community legal service affiliated with Monash University, Melbourne, Australia, and partly funded by Victoria Legal Aid. MOLS deals with a range of legal matters, including: criminal law, family law, tenancy and neighbourhood disputes, and a number of credit, debt, and bankruptcy issues. In July 2010, the Multi-Disciplinary Clinic (MDC) was established at MOLS to provide a holistic service to clients by involving students from three academic disciplines to deal with client issues. This paper describes some of the mechanics of how the MDC operates, including how students are assessed and supervised. It also describes the benefits for students and clients. Importantly, the paper examines the contributions of each discipline to a broader goal of integrating equally important skill bases to deliver solutions for clients.

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)0.004
metaresearch head score (Gemma)0.005
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.003
Scholarly communication0.0030.002
Open science0.0020.012
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0090.001

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 head0.092
GPT teacher head0.473
Teacher spread0.381 · 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
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

Citations1
Published2014
Admission routes1
Has abstractyes

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