Does Gender Play a Role?: Judges and Trial Outcomes in Sexual Assault Cases in Canada
Bibliographic record
Abstract
Canada undertook a major reform of the laws surrounding rape beginning in 1983. As a result of this reform, rape was re-conceptualized as ‘sexual assault’, and laws surrounding consent and earlier sexual history were introduced in 1992. These changes were greatly influenced by feminist groups in the hope that victims of sexual violence would be more likely to come forward. However, sexual assault continues to be underreported in Canada and there is substantial attrition from the number of reported cases to the number of charged and convicted cases. Such attrition raises questions about other areas in which sexual assault legal reforms have not lived up to their promise. Media reports and some scholarly literature suggests inappropriate actions are or are likely occurring within the Canadian Criminal Justice System (CCJS) at the hands of judges; however, the present study indicates that such behaviours are infrequent. Through a cross-sectional exploratory study design with a content analysis of judge rulings, this study explores the impact of judge and victim gender on sexual assault trial outcomes. Further, the present study explores whether popular rape myths still embed themselves within the CCJS, despite legislative efforts to eliminate such myths. Using the CanLII database, adult sexual assault cases within Canada between 2000 and 2018 with either a written verdict or sentencing decision (N=106) were identified and coded on a variety of variables. Results indicate that the gender of both the judge and the victim is not associated with trial outcomes, judges are not likely to use common rape myths—albeit it does occur from time to time, are likely to follow the CCC, and judges are more likely to use language that resembles the violent connotations of sexual assault rather than sexualizing the assault. These results may be cause for cautious optimism for policy-makers, researchers, and feminist advocates alike.
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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 | 0.004 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 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) | 0.003 | 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 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".