Court, Judges and the Pandemic: Computational Legal Insights from the Ontario Court of Appeal Corpus 2008-2021
Bibliographic record
Abstract
Appellate courts occupy a unique position. They are the final instance for most litigants guiding lower courts but they are also a gateway to the Supreme Court. This dual role calls for special scrutiny and analysis. Yet, data and analysis of appeal courts remains scarce especially compared to apex courts. This article fills part of this gap relating to the Ontario Court of Appeal. It introduces a new dataset of its decisions between 2008-2021 consisting of both metadata, such as outcomes per decision, and the decision full text, which can be mined through natural language processing techniques. Aside from presenting the dataset, the paper uses novel data science approaches to trace the practice of the Court over time, to dissect the decision patterns of its judges, and to assess how the pandemic shock impacted the Court. It finds, amongst others, that the Court has been stable in its decision patterns, but that decisions have grown longer; it also shows that some judges render harsher decisions than others, and it illustrates how the pandemic created instant precedent. We hope that the new dataset and corpus will spur further research on the Ontario Court of Appeal.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".