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Record W5293378 · doi:10.60082/0829-3929.1192

When Law Reform Is Not Enough: A Case Study on Social Change and the Role that Lawyers and Legal Clinics Ought to Play

2014· article· en· W5293378 on OpenAlexvenueaboutno aff
Jeff Carolin

Bibliographic record

VenueJournal of Law and Social Policy · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Education and Practice Innovations
Canadian institutionsnot available
FundersRoyal College of Anaesthetists
KeywordsGrassrootsLaw reformPolitical scienceLegal educationLawLegal professionScholarshipInjusticeEmpirical legal studiesSociologySocial movementPolitics

Abstract

fetched live from OpenAlex

Based on his experience as a law student in the clinical legal education program at Parkdale Community Legal Services in 2010, the author draws on poverty law scholarship to better understand his frustrations with a law reform campaign he worked on related to refugee family reunification. The scholarship’s central critique of law reform campaigns is that they are excessively narrow: they focus on a particular law and construct the law itself as the social injustice. This leads to two subsidiary problems. First, law reform campaigns ignore the underlying socio-political context that produced the law, foregoing opportunities for broader societal transformation. Second, law reform campaigns position lawyers as the agents for social change, missing an opportunity for movement building and even disempowering affected communities. In applying these critiques, the author articulates the failure of the clinic’s campaign to address Canada’s xenophobic and classist approach to border control, and to build social movements to challenge this approach. An alternative strategy for law reform that addresses some of these problems is embodied in Collaborative Legal Play, or CLAY, a grassroots organization that creates workshops on legal topics using principles of popular education and game-based and theatre-based facilitation.

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)0.000
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: Empirical
Teacher disagreement score0.868
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.001
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 head0.145
GPT teacher head0.445
Teacher spread0.300 · 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

Citations3
Published2014
Admission routes2
Has abstractyes

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