Let’s Talk About Sexual Assault: Survivor Stories and the Law in the Jian Ghomeshi Media Discourse
Bibliographic record
Abstract
The recent allegations against former Canadian radio host Jian Ghomeshi catalyzed an exceptional moment of public discourse on sexual assault in Canada. Following public revelations from several women who described being attacked by Ghomeshi, many others came forward with accounts of sexual violence in their own lives. Affirming feminist critiques of sexual assault law reform, many survivors drew on their experiences to expose the criminal justice system’s ongoing flaws in processing sexual assault cases. While some held out hope for the criminal law’s role in addressing sexual violence, most rejected its individualizing and retributive aspects. Instead, survivors emphasized the need for their experiences to be meaningfully acknowledged, and the primary importance of speaking out publicly about sexual violence in order to debunk common stereotypes and effect cultural change. Following a grassroots feminist impetus, they framed their stories as resisting legal and social norms through a turn to direct personal experience. Yet the experiential accounts of sexual violence publicized in the wake of Ghomeshi also drew from criminal law discourse in a number of ways. Not only did survivors use legally grounded concepts to define their experiences, they also re-theorized past experiences in ways that bear noticeable parallels to recent shifts in the law of sexual assault, especially around consent. Thus, I argue, their accounts should be read as both resisting and reflecting legal scripts.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.029 | 0.018 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".