Trial by Zoom? The Response to COVID-19 by Canada's Courts
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 it