Does a Judge's Party of Appointment or Gender Matter to Case outcomes?: An Empirical Study of the Court of Appeal for Ontario
Bibliographic record
Abstract
A recent study by Cass Sunstein identified ideological differences in the votes cast by judges on the United States Courts of Appeals in certain types of cases. He found that these patterns varied depending on the ideology of an appellate judge's co-panelists. In this study, we undertake a similar examination of the busiest appellate court in Canada, the Court of Appeal for Ontario. This study collects data on the votes cast by individual judges in every reported decision between 1990 and 2003. Each case was cod6d by type, for example "criminal law," "constitutional law," or "private law." In addition, the votes cast by individual judges in each category were tracked based on variables such as the type of litigant, the political party that appointed the judge, and the judge's gender. This study reveals that at least in certain categories of cases, both party of appointment and gender are statistically significant in explaining case outcomes. Between these two variables, gender actually appears to be the stronger determinant of outcome in certain types of cases. While these findings are cause for concern, this study also points toward a simple solution. Diversity in the composition of appeal panels both from the standpoint of gender and party of appointment dampened the statistical influence of either variable. In other words, in the case of gender, a single judge on a panel who is of the opposite sex from the others, or in the case of political party, a single judge appointed by a different political party, is sufficient to eliminate the potential distorting influence of either variable. This finding suggests a need to reform how appeal panels are currently assembled in order to ensure political and gender diversity and minimize concerns about the potential for bias.
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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".