The missing and imagined perpetrator in rape prevention efforts
Bibliographic record
Abstract
In response to unceasing rates of sexual assault, and the failure of statutory interventions to reduce the prevalence of sexual violence, several prevention strategies have emerged.Over the past fifty years, initiatives have included awareness raising campaigns, provision of self-defence training, promotion of rape alarms, and education-based efforts in the form of bystander intervention and consent training workshops aimed at encouraging prosocial action to reduce sexual violence.More recently, a striking array of technologies has emerged claiming the capacity to prevent or mitigate the risk of sexual violence including apps that harness the communication functions of smart technology and a variety of 'wearables' designed to protect the body from assault or repel a would-be assailant.In this paper we analyse these prevention initiatives in the modern period, demonstrating that what is striking about the majority is the relative absence of the perpetrator in both design and endorsement.Where an assailant is alluded to, this 'imagined perpetrator' tends to reflect stereotypical constructions of how sexual violence occurs and who commits it.The consequence of such representations is that many prevention efforts place responsibility onto potential victims to protect themselves, contributing further to widespread misunderstandings about the realities of rape and rapists.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".