Meaningful Justice Design: A Practical Implementation of Accessible Justice
Bibliographic record
Abstract
Traditional legal systems often include barriers to access to justice, and the integration of technology frequently fails to deliver user-centric solutions. This thesis investigates how user-centric design methodologies can optimize technology integration to enhance access to justice while mitigating barriers. It proposes “Meaningful Justice Design” (MJD), a novel methodology aiming to make justice systems responsive to users’ needs for understanding, navigation, and effective problem resolution. This research encompasses a literature review, a meta-analysis of empirical data, and an examination of Canadian and international case studies, revealing that contemporary justice processes often lack genuine user-centricity. MJD is organized around five core tenets: Justice is the Goal, Injustice is the Metric, User-Centricity, One-Size-Fits-Some Resolution Pathways, and Transformative Integration of Technology. MJD provides a framework for systemic justice reform with significant implications, advocating for system designs that are not only procedurally sound but also foster accessible, equitable, and restorative experiences for all users.
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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 | 0.068 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".