The Court System in a Time of Crisis: COVID-19 and Issues in Court Administration
Bibliographic record
Abstract
Canadian courts, in many ways, are neither efficient nor effective. This has been clear for many years. This article looks broadly at how little attention has been paid to court administration in the past, especially during times of crisis, and examines the impact the current pandemic may have on the future of Canadian court administration. In this vein, we examine emergency plans in general before turning to pandemic-specific plans and how, especially in Canada, these have been found wanting during this current crisis. Like most organizations, courts have developed plans – business contingency (BCPs) in Canada and continuity of operation (COOPs) in the United States—laying out policies and processes to follow in an emergency. Yet none of the various disaster plans created by courts in both Canada and the United States highlight the role and importance technology would play. Despite the increasing use of remote access for hearings—there has been a great deal of success in scheduling appeal hearings remotely—most courts have been unable to operate at pre-pandemic levels. In fact, most courts have postponed the majority of their hearings and have had to push dockets forward. Postponing a large portion of the courts’ hearings will undoubtedly add to a backlog of cases and increase the administrative burden on operations once physical distancing is removed. But the change in attitude that has taken place over the past few months is arguably greater than the sum of all changes made over the last forty years since Carl Baar’s reference to courts being “horse-and-buggy” organizations. The pandemic has provided a perfect occasion—no doubt forced but relatively low-risk—to try new things. Our position is that steps need to be taken to ensure that an increased reliance on “privileged access to technology” during COVID-19 does not lead to an “exacerbation of denial of access to justice.”
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.067 | 0.038 |
| Scholarly communication | 0.031 | 0.008 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.011 | 0.017 |
| Insufficient payload (model declined to judge) | 0.010 | 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".