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Record W2326021517 · doi:10.5553/elr.000051

Canadian Civil Justice: Relief in Small and Simple Matters in an Age of Efficiency

2015· article· en· W2326021517 on OpenAlexaboutno aff
Jonathan Silver, Trevor C. W. Farrow

Bibliographic record

VenueErasmus Law Review · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Education and Practice Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsEconomic JusticeAusterityPolitical scienceOptimismPublic administrationPublic relationsLawPoliticsPsychologySocial psychology

Abstract

fetched live from OpenAlex

Canadian Civil Justice: Relief in Small and Simple Matters in an Age of Efficiency Canada is in the midst of an access to justice crisis. The rising costs and complexity of legal services in Canada have surpassed the need for these services. This article briefly explores some obstacles to civil justice as well as some of the court-based programmes and initiatives in place across Canada to address this growing access to justice gap. In particular, this article explains the Canadian civil justice system and canvasses the procedures and programmes in place to make the justice system more efficient and improve access to justice in small and simple matters. Although this article does look briefly at the impact of the global financial crisis on access to justice efforts in Canada, we do not provide empirical data of our own on this point. Further, we conclude that there is not enough existing data to draw correlations between austerity measures in response to the global crisis and the challenges facing Canadian civil justice. More evidence-based research would be helpful to understand current access to justice challenges and to make decisions on how best to move forward with meaningful innovation and policy reform. However, there is reason for optimism in Canada: innovative ideas and a national action plan provide reason to believe that the country can simplify, expedite, and increase access to civil justice in meaningful ways over the coming years.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.959
Threshold uncertainty score0.302

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.086
GPT teacher head0.396
Teacher spread0.311 · 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 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

Citations0
Published2015
Admission routes1
Has abstractyes

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