‘But her age was not given on her Facebook profile’: minors, social media, and sexual assault trials.
Bibliographic record
Abstract
This paper examines the role of social media evidence in sexual assault trials in Canada, focusing on cases with underage female victims. Teenage girls are among the heaviest social media users and face unique expectations regarding the performance of heteronormative gender norms. Society simultaneously encourages girls to enact gender roles that emphasize their femininity and sexuality and punishes them for acting according to these standards. When girls engage in performativity online, they leave behind a digital footprint that can be used against them at trial. Through a detailed case study of 14 publicly available judgments, we analyze how judges evaluate girls’ social media content in sexual assault trials that feature a mistake of age defense. Drawing on social media research, legal studies, and the concept of performativity, we show that judges vary greatly in their understandings of gender norms and that this translates to divergent case outcomes. In the ‘guilty’ cases, the judges contextualize social media content as insufficient and unreliable, noting that it is common for youth to lie or embellish facts online. In the ‘not guilty’ cases however, the judges appear to take such evidence at face value and hold girls accountable for having provocative pictures or misrepresenting themselves online. Such practices are problematic because they perpetuate rape myths and misconceptions about victim behavior. We call for greater consideration of the socio-cultural norms that govern girls’ social media use to avoid biased interpretations that adversely shape the outcome of sexual assault trials.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.011 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".