"An Aura of Disbelief:" Rape Mythology and Victim Blaming in the Legal Response to Disclosure of Sexual Violence
Bibliographic record
Abstract
This research study focuses on sexual violence (SV) in Canada, which one in three women will experience during their lives. Yet, even though the prevalence of violence against women in Canada is so significant, only one in ten survivors will report their experience of SV to the criminal justice system (CJS). Previous literature has identified the limited number of reports to authorities in Canada as being related to rape mythology. Due to the influence of rape mythology, a notion of a “good versus bad victim” is often used to deem which survivors are innocent and credible versus responsible or blamed for their victimization. Canadian legal and feminist scholars, such as Melanie Randall (2010), Elizabeth Sheehy and Holly Johnson (2012), have maintained that survivors do not trust the CJS’s response because the CJS dismisses a majority of SV complainants as “unfounded,” meaning the responding officer believed the crime had not occurred. Using an intersectional feminist theoretical framework, this study investigates if rape myth acceptance and victim blaming play a role in the Canadian CJS’s response to disclosures of SV. Through semi-structured qualitative interviews with five SV professionals, participants discussed their interactions with the CJS and how they perceive the legal responses’ impact on survivors of SV. Participant’s stressed that CJS was not built to support survivors of SV nor the individuals’ most likely to experience violence, which was reflected through participants’ discussions around using the term, legal system or “prison industrial complex,” rather then CJS. The research findings highlight that the Canadian legal system has not provided justice or support for survivors of SV, but rather survivors’ credibility as a complainant has been measured against rape mythology and the construction of the “good or ideal victim.” This research study further argues that survivors who engage with the legal system are met with victim blame and self-blame, which has been represented through the prevalence of “unfounded” cases in SV crimes. The Canadian legal system needs to be changed and re-structured in order to provide support for survivors and to uphold a feminist and survivor-centered framework. But until that change occurs, the system will continue to cause harm and oppression against those most vulnerable to violence.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.039 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".