Implementing technology in the justice sector: A Canadian perspective.
Bibliographic record
Abstract
Despite the many technological advances that could benefit the court system, the use of computers and network technology to facilitate court procedures is still in its infancy, and court procedures largely remain attached to paper documents and to the physical presence of the parties at all stages. More and more research is focusing on the use of technology to make the legal system more efficient and to reduce excessive legal costs and delays. The goal of this exploratory research project is to examine the experience of justice sector technology implementation from the perspective of individuals involved first-hand in the implementation process. This study will provide insight into the political and cultural factors that support and hinder the implementation of technologies in the justice sector. Unstructured interviews were conducted with individuals involved in the planning and implementation of technological change in Canadian courts in order to gather their perspectives on the change process. These key informants were asked to discuss the process of technological change in their courts, the barriers that they experienced to such technological change, and the factors that promote or support the implementation of technology by courts. A grounded theory approach was used to identify emergent themes related to these questions. The results provide insight into the factors that promote and impede the implementation of technologies by Canadian courts.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.046 | 0.022 |
| Scholarly communication | 0.018 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".