Designing and Implementing e-Justice Systems: Some Lessons Learned from EU and Canadian Examples
Bibliographic record
Abstract
Access to justice has become an important issue in many justice systems around the world. Increasingly, technology is seen as a potential facilitator of access to justice, particularly in terms of improving justice sector efficiency. The international diffusion of information systems (IS) within the justice sector raises the important question of how to insure quality performance. The IS literature has stressed a set of general design principles for the implementation of complex information technology systems that have also been applied to these systems in the justice sector. However, an emerging e-justice literature emphasizes the significance of unique law and technology concerns that are especially relevant to implementing and evaluating information technology systems in the justice sector specifically. Moreover, there is growing recognition that both principles relating to the design of information technology systems themselves (“system design principles”), as well as to designing and managing the processes by which systems are created and implemented (“design management principles”) can be critical to positive outcomes. This paper uses six e-justice system examples to illustrate and elaborate upon the system design and design management principles in a manner intended to assist an interdisciplinary legal audience to better understand how these principles might impact upon a system’s ability to improve access to justice: three European examples (Italian Trial Online; English and Welsh Money Claim Online; the trans-border European Union e-CODEX) and three Canadian examples (Ontario’s Integrated Justice Project (IJP), Ontario’s Court Information Management System (CIMS), and British Columbia’s eCourt project).
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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 | 0.019 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.017 | 0.010 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".