Visualizing sexual assault: An exploration of the use of optical technologies in the medico-legal context
Bibliographic record
Abstract
This article is an exploration of the visualization of sexual assault in the context of adult women. In investigating the production of visual evidence, we outline the evolution of the specialized knowledge of medico-legal experts and describe the optical technologies involved in medical forensic examinations. We theorize that the principles and practices characterizing medicine, science and the law are mirrored in the medico-legal response to sexual assault. More specifically, we suggest that the demand for visual proof underpins the positivist approach taken in the pursuit of legal truth and that the generation of such evidence is based on producing discrete and decontextualized empirical facts through what are perceived to be objective technologies. Drawing on interview and focus group data with 14 sexual assault nurse examiners (SANEs) in Ontario, Canada, we examine perceptions and experiences of the role of the visual in sexual assault. Certain of their comments appear to lend support to our theoretical assumptions, indicating a sense of the institutional overemphasis placed on physical damage to sexually assaulted women's bodies and the drive towards the increased technologization of visual evidence documentation. They also noted that physical injuries are frequently absent and that those observed through more refined tools of microvisualization such as colposcopes may be explained away as having resulted from either vigorous consensual sex or a "trivial" sexual assault. Concerns were expressed regarding the possibly problematic ways in which either the lack or particular nature of visual evidence may play out in the legal context. The process of documenting external and internal injuries created for some an uncomfortable sense of fragmenting and objectifying the bodies of those women they must simultaneously care for. We point to the need for further research to enhance our understanding of this issue.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".