A New Lens: Reframing the Conversation about the Use of Video Conferencing in Civil Trials in Ontario
Bibliographic record
Abstract
The state of courtroom technology in Ontario is increasingly capturing the attention of both the public and the legal profession. This article seeks to contribute to the conversation on this issue by focusing on one particular technology in Ontario’s courtrooms: the use of video conferencing to receive witness testimony in civil trials. The central claim is that the approach to video conferencing that dominates the policy discourse reflects an overly narrow, instrumentalist view of technology that fails to adequately take account of possible broader political and social implications as well as this technology’s transformative potential. This argument is developed by exploring two different sources of risk associated with the implementation of video-conferencing technology in civil trials: (1) how video conferencing, as a mediating technology, may unintentionally interfere with credibility assessments and emotional connections between courtroom participants; and (2) the ways in which video conferencing, by disrupting the physical geography of adjudication, threatens the solemnity associated with, and respect given to, the civil justice system. A detailed consideration of these risks reveals that video conferencing engages fundamental questions about our civil justice system and implicates democratic values in ways that require more nuanced consideration in conversations about its use. Rather than offer a final verdict on the use of video conferencing in civil trials in Ontario, this article concludes by calling for deeper and broader discourse on this issue. This discussion should include all stakeholders in a conversation about if and how video-conferencing technology should be incorporated into our civil justice system.
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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 | 0.016 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.068 | 0.052 |
| Scholarly communication | 0.019 | 0.011 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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".