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Record W2302325230

Institutionalizing Access to Justice: Judicial, Legislative and Grassroots Dimensions

2007· article· en· W2302325230 on OpenAlexaffabout
Faisal Bhabha

Bibliographic record

VenueSSRN Electronic Journal · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Education and Practice Innovations
Canadian institutionsYork University
Fundersnot available
KeywordsLegislatureGrassrootsEconomic JusticePolitical scienceSupreme courtDoctrineLawValue (mathematics)Law and economicsPublic administrationSociologyPolitics
DOInot available

Abstract

fetched live from OpenAlex

It is well understood that there can be no legal right without a remedy and, further that the remedy must be accessible if it is to be meaningful. In reality, however, the pragmatic concerns associated with effectuating access-to-justice have proven complex. Of particular concern is how best to ensure access-to-justice for those who lack the financial means to litigate. This concern has taken on a particular importance in light of two recent Supreme Court of Canada decisions on advanced costs and the right to legal aid, as well as the Government of Canada’s recent cancellation of the Court Challenges Program. The current deficit in access-to-justice programs suggests that a more multi-faceted approach involving judicial initiatives, legislative programs and coordination amongst social activists is needed to uphold the constitutional value of access to the justice system. The author argues, through doctrinal, theoretical and case study analysis, that increasing access-to-justice necessarily entails taking positive steps to create access to the courts, rather than relying solely upon the inherent limitations of judicial pronouncements and doctrine. Further, the author suggests that this multi-faceted approach would encourage lawyers to empower communities, demystify the law and contribute to the institutionalization of access-to-justice.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.692
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.001
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 head0.053
GPT teacher head0.430
Teacher spread0.377 · 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 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

Citations4
Published2007
Admission routes2
Has abstractyes

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