Virtual Justice: A Complex Portrait of Canadian Self-Represented Litigant Experiences with Virtual Hearings
Bibliographic record
Abstract
“Virtual Justice: A complex portrait of Canadian self-represented litigant experiences with virtual hearings” is the result of a year-long project generously funded through a grant from the McLachlin Fund, with the goal of understanding the experiences of Canadian self-represented litigants (SRLs) with virtual hearings since the onset of the pandemic, when such processes began to dramatically increase and become much more common.\nUsing a survey and focus groups, we gathered data from many SRLs with experiences across jurisdictions and types of legal matter. The results reflect the fact that SRLs’ experiences with virtual hearings are, in fact, quite varied. Approximately 24% of the SRLs surveyed were satisfied with their virtual hearing experience, while 35% were dissatisfied, and 15% reported they were neither satisfied nor dissatisfied. This report dives into the demographics and specific contexts behind these numbers, and seeks to understand both the positive and negative engagements SRLs have had with virtual hearings, especially as these engagements can give insight into how courts and systems might improve virtual processes for the most vulnerable stakeholders. The report concludes with both recommendations for improvement, and suggestions for further research on this topic.
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.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".