Bibliographic record
Abstract
Ontario’s civil jury system has been the topic of many discussions about reform. However, none of these explorations contemplated the drastic effects of ever-evolving public health emergency. The COVID-19 pandemic has heightened Ontario’s access to civil justice crisis through extensive pandemic delays, while simultaneously challenging the role of civil jury in administering justice. Ontario consequently provides a ripe case study to explore how the pandemic has affected civil jury trials and to explore ways of enhancing their viability in a post-pandemic Ontario. This this article is concerned with advancing measures that can not only enhance the viability of civil jury trials going forward, but advance access to civil justice more generally. The purpose of this article is twofold. First, to examine how the pandemic has fundamentally challenged the viability of civil jury trials while exacerbating existing impediments to accessing civil justice. And second, to outline a multifaceted approach to reforming the jury trial to ensure it remains a viable vehicle for civil justice consistent with enhancing access to justice through the pandemic. The hope is that this article will inspire much needed exploration into Ontario’s civil justice system to address the consequences of the COVID-19 pandemic and future emergencies.
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 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 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.011 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.047 | 0.006 |
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".