Documenting Mass Rape: Medical Evidence Collection Techniques as Humanitarian Technology
Bibliographic record
Abstract
Aim: Emerging global networks of human rights activists, doctors, and nurses have advocated for increased collection of medical evidence in conflict-affected countries to corroborate allegations of sexual violence and facilitate prosecution in international and domestic courts. Such initiatives are part of broader shifts in human rights advocacy to document human rights violations using rigorous, standardized methodologies. In this paper, I consider three principal forms of medical evidence to document sexual violence and their use in these settings: the patient medical record, the medical certificate, and the sexual assault medical forensic exam (commonly known as the “rape kit”). Methods: Combining archival research with interviews of activists, healthcare practitioners, lawyers, investigators, and other experts, I trace the collection and use of medical evidence to document mass rape since the establishment of the International Criminal Tribunals for Rwanda and the former Yugoslavia. Results: The use of medical evidence collection techniques to document sexual violence during and shortly after armed conflict or mass violence against civilians is still relatively new and not well institutionalized. When available, medical evidence has been used to document patient disclosures, describe patterns of crime, prompt investigation, issue indictments, and provide context evidence to establish international crimes occurred. Conclusions: Drawing on approaches in science and technology studies, law and society, and cultural sociology, I argue that medical evidence collection techniques represent an emerging humanitarian technology that may influence what comes to count as sexual violence, which crimes are deemed justiciable, and ultimately how events come to be remembered, within and beyond courts.
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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 | 0.149 | 0.246 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.013 |
| Science and technology studies | 0.006 | 0.035 |
| Scholarly communication | 0.026 | 0.025 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".