Imagining Law: Curated Narratives of Sexual Assault in The Ghomeshi Effect
Bibliographic record
Abstract
This project explores how myths of law are aesthetically animated.I unpack narratives of sexual violence, trauma, and social transformation presented in the verbatim theatre performance of The Ghomeshi Effect.I suggest that while the producers of the play claim that the verbatim methods authentically tell experiences of sexual violence, their representation of these narratives reproduce broader myths of law as an ordered system of truth.Stories of sexual violence in this performance are not authentic representations, but an aesthetic platform through which the power of law is animated.often not heard by medical and legal models in meaningful ways.Alternatively, peer support work, lead by community volunteers who are extensively trained in knowledge about the intersections of gendered and institutionalized violence are the basis of the Centre's outreach framework.Rather than a complaint or reporting process, rape crisis centres often focus on providing confidential spaces, like crisis lines, one-on-one support sessions, or peer-support focus groups.These centres work within principles of belief and active listening.4 During my training, the lessons consistently iterated that, unlike medical and legal models of reporting, rape crisis centres that focus on feminist, intersectional, and peer-support programming provide spaces for victims to authentically represent their own stories of violence and trauma.5 Peer support methods are seen to provide more meaningful support than law or medicine because of their perceived ability to authentically represent victims of sexual violence.Rape crisis centres that work with this model hold the perception that law often distorts experiences of sexual violence by placing them inside institutional influences that are considered to prioritize white-male experiences.Peer support models pose a contrast between rape crisis centres and law, specifically in their abilities to provide an authentic representation of sexual assault victim experiences, claiming that these centres can be a space outside of institutional biases.The timing of my peer-support training overlapped with the high-profile case of Jian Ghomeshi, a Canadian radio personality who was accused of sexually assaulting several women he had worked with at the CBC.Naturally, he was the topic of discussion 4 The principles of belief and active listening mean that when a service user comes to a centre or calls a crisis line, their stories and experiences with violence are to be acknowledged as truth and never judged.This is a main reason how rape crisis centres distance themselves from legal or medical models, noting that the centres do not take reports or spearhead investigations; they are meant to provide support and trauma counselling.5 Information gathered here about support work was gained during my peer support training sessions in Spring of 2016.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.031 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".