Trial by Zoom? The Response to COVID-19 by Canada's Courts
Bibliographic record
Abstract
COVID-19 has made videoconferencing a regular occurrence in the lives of Canadians. Videoconferencing is being used to maintain social ties, run business meetings—and to uphold responsible government. On April 28, 2020, Members of the House of Commons sat virtually using Zoom. The virtual sitting was the first of what will become a stand-in for regular proceedings, allowing the Members to fulfill some of their parliamentary duties while complying with physical distancing (see Malloy, 2020). As the legislative and executive branches look to digital technology to allow the business of government to continue, what about the judicial branch of Canada's government? Courts are an essential service. This is best articulated by the Chief Justice of Nova Scotia: “The fact is, the Courts cannot close. As the third branch of government, an independent judiciary is vital for our Canadian democracy to function. It is never more important than in times of crisis” (Wood, 2020). In this analysis, we seek to understand how courts have responded to COVID-19 and the challenges of physical distancing through the use of digital technologies. This is accomplished through a systematic review of COVID-19 statements and directives issued from all levels of court across Canada. We briefly compare Canada to the United States, a jurisdiction that demonstrates greater openness to technology.
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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 | 0.030 | 0.122 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.018 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".