Effecting a Culture Shift—An Empirical Review of Ontario’s Summary Judgment Reforms
Bibliographic record
Abstract
Lawyers and policymakers in Canada frequently discuss the need for reforms to increase access to civil justice, but concrete efforts to improve the efficiency and cost-effectiveness of our justice system are few and far between. Unfortunately, even when reforms are implemented, measures are rarely put in place to assess whether the reforms were effective. Ontario’s Civil Justice Reform Project inspired a package of amendments to Rules of Civil Procedure in 2010 but, aside from anecdotal reports, little is known about whether they achieved their desired effects. This paper presents an empirical analysis of all reported summary judgment decisions in Ontario between 2004 and 2015, in order to explore whether amendments to the summary judgment rules actually improved the efficiency and affordability of the civil justice system as was intended. By reviewing trends in the number and outcomes of summary judgment motions throughout the study period, we can conclude that the amendments to Ontario’s summary judgment rules have made strides towards their intended goal. Since the reforms, we observe an increase in the number of summary judgment motions determined, an increase in the number of summary judgment motions granted, and, broadly, an increase in the proportion of successful summary judgment motions. The data analyzed in this study suggest that the “culture shift” promoted by the Supreme Court of Canada following the implementation of the new rule is in fact underway.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.019 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".