Digitizing rape culture: Online sexual violence and the power of the digital photograph
Bibliographic record
Abstract
The damaging effects, for both the victims and perpetrators, of photographing sexual assault should be self-evident. However, in the cases of Rehtaeh Parsons, Jane Doe and Audrie Pott, photographs of sexual violence seem to have been taken and digitally disseminated without regard for the possible consequences. Thus, these cases pose disturbing questions about the ways that sexual violence is normalized and legitimized in western culture and the ways that new media is implicated in this process. These cases demonstrate how the ubiquity and permanence of digital photographs create new concerns for victims of sexual violence and new questions regarding the interpretive matrix of photographs. Using Judith Butler’s theory on photography, torture and framing, I argue that these cases are an example of what Butler refers to as the digitalization of evil. Through this framework, I will discuss the ways that new media exacerbates experiences of sexual violence and examine issues surrounding the interpretation of photographs of sexual 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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".