Technology and the Justice System
Bibliographic record
Abstract
Chapter 1 examines the integration of technology into the justice system, highlighting the historical reluctance of courts to fully embrace digital advancements due to technological limitations, traditional perceptions of court authority and inclusivity concerns. The COVID-19 pandemic accelerated the adoption of digital courts, marking a significant shift towards long-term judicial reform. The concept of “smart courts” is introduced, characterized by sophisticated, comprehensive and automated processes that distinguish them from earlier e-court models. The chapter outlines the components of smart courts, including digital litigation services, AI-assisted adjudication, automated enforcement, advanced case management and robust governance. A historical overview traces the stages of technological integration, from computerization to automation, and provides comparative analyses of smart court initiatives in China, England, Singapore, the USA, Canada and Italy. The chapter concludes by emphasizing the global trend towards judicial digitalization and sets the stage for a deeper exploration of specific technological applications in subsequent chapters.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".