Technology-Driven Changes in an Organizational Structure: The Case of Canada’s Courts Administration Service
Bibliographic record
Abstract
Recently, the federal Courts Administration Service of Canada (the CAS) announced its plans to implement an automated case and registry management system (CRMS) in Canada’s Federal Courts: the Federal Court of Appeal, the Federal Court, the Tax Court and the Court Martial Appeal Court of Canada. The CRMS embraces many functions that contribute to the courts’ daily operations: case management, access to case records and documents, transmission and service of court records, transfer of cases and documents among courts, scheduling of cases and courtrooms, etc. Traditionally, these services have been provided by courts’ registries. However, integrated systems offer an opportunity to automate most processes, thereby improving operating efficiencies and reducing procedural delays.Despite the significant benefits of digitization and automation of courts’ operations, the implementation of a CRMS solution poses significant risks to judicial independence. Particularly, this article scrutinizes how CRMS may undermine the security of judicial information and how automation of procedures may adversely affect the procedural independence of the judiciary. To minimize these risks, this article suggests that the CAS create a specialized standard-setting committee that will monitor the CRMS implementation process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.040 | 0.018 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".