Virtual court hearings: judicial perspective from Ontario judgments
Bibliographic record
Abstract
The onset of the Covid-19 pandemic has sparked a phenomenal rise in virtual hearings in Ontario (Canada), much like in many other jurisdictions around the world. While video court participation has been available for decades, hearings have, until recently, remained courtroom-centric events and the possibility of dematerialized courts had seemed far off, if not far-fetched. Inspired by the latest evolution of justice delivery, this thesis explores the views of Ontario judges on the implications of virtual justice. What judges have to say about video-enabled testimony and adjudication matters enormously given their role in the administration of justice. Through a systematic content analysis of reported judicial decisions from the superior trial court and appellate court in Ontario between 16 March 2017 and 17 December 2021, this thesis tackles the question: To what extent do reported decisions by Ontario judges confirm or challenge concerns about virtual hearings expressed in scholarly debates about the topic? The analysis suggests that judges share many of the concerns articulated by scholars about proceeding virtually, such as the loss of solemnity and the expanded opportunity it lends to remote participants to behave in ways that undermine the integrity of the trial. At the same time, judges agree that proceeding virtually can be effective, fair, and in some cases, a more efficient alternative to the in-person format. The findings further reveal how judges have adapted their hearing management practices to accommodate the virtual court environment and assert control over remote sites. Overall, judicial conceptions of the modern trial appear to have evolved to the point that it is no longer seen as inevitably a courtroom affair.
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.032 | 0.018 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".