Multi-Functional Access to Justice Centres
Bibliographic record
Abstract
In recent years, the integration of digital technology into the Canadian judicial system has accelerated, driven by both technological advancements and the urgent needs highlighted by the COVID-19 pandemic. This article explores the transformative potential of digital justice within Canadian courts, focusing on a proposal to repurpose circuit court facilities as "Access to Justice Centres" [AJCs]. These centers aim to address existing access to justice issues by providing state-of-the-art digital interfaces and centralizing court functions while preserving the dignity and decorum of in-person proceedings. Our analysis evaluates the successes and challenges of digital technologies in judicial processes, informed by empirical research with Canadian judges. The findings suggest that while digital tools have enhanced judicial efficiency and access to justice, significant disparities remain, particularly for marginalized communities. By leveraging user-centric design principles and existing infrastructure, AJCs could offer innovative solutions to bridge these gaps, ensuring that digital justice benefits all sectors of society. This article contributes to the ongoing dialogue on judicial reform, emphasizing the need for a thoughtful and inclusive approach to integrating technology in the administration of justice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.015 | 0.008 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.041 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".