MétaCan
Menu
Back to cohort
Record W1058365201 · doi:10.60082/0829-3929.1028

The Critical Characteristics of Community Legal Aid Clinics in Ontario

2004· article· en· W1058365201 on OpenAlexvenueaboutno aff
Lenny Abramowicz

Bibliographic record

VenueJournal of Law and Social Policy · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Education and Practice Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsSociologyPolitical scienceGeography

Abstract

fetched live from OpenAlex

Cet article examine le systhme de cliniques juridiques communautaires en Ontario en cette ann6e de son 30e anniversaire, et se penche tout sp~cialement sur les raisons de son succhs remarquable.I1 souligne le fait qu'alors que, dans d'autres juridictions, les services d'aide juridique ont r~tr~ci, le systhme de cliniques communautaires en Ontario a en fait grandi, et est maintenant universellement reconnu pour la solidit6 des services juridiques qu'il offre aux collectivit~s A faible revenus.L'article d~clare que les cliniques communautaires posshdent TROIS << caract6ristiques essentielles )) et que ce sont ces caract6ristiques qui expliquent leur succhs.Ces caract~ristiques essentielles sont : gouvernance par la collectivit6 locale; concentration sur des prestations de services juridiques pour les personnes et les collectivit~s d~favoris~es; et, offre d'une gamme vari~e de services pour satisfaire aux besoins de leurs collectivit~s.Dans des p6riodes d'6volution des besoins et de contraintes financihres, il est parfois tentant pour certaines personnes d'essayer de changer des programmes qui marchent trhs bien, ou d'essayer de les utiliser A des fins pour lesquelles ils n'avaient pas 6 cr6s.C'est particulihrement en ces temps de grande pression qu'il faut se rappeler des raisons qui avaient motiv6 la creation des cliniques communautaires, et comment leurs caract6ristiques uniques furent fa~onnes pour r~pondre A un ensemble bien particulier de besoins juridiques.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.813
Threshold uncertainty score0.774

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.091
GPT teacher head0.456
Teacher spread0.364 · 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

Citations2
Published2004
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Law and Social PolicySame topicLegal Education and Practice InnovationsFrench-language works237,207