Bibliographic record
Abstract
How has Ontario's Sexual Assault Evidence Kit (SAEK) developed over the past thirty years, what purpose does it serve and whom does it benefit?These questions are at the heart of Andrea Quinlan's highly nuanced analysis, The Technoscientific Witness of Rape.In six clearly written and compellingly argued chapters, Quinlan contextualises the SAEK, exposing the tensions, contradictions and controversies surrounding its development and use, and demonstrates that it has failed to deliver to victims of sexual violence the justice it promises.Quinlan introduces the central theoretical, methodological and conceptual resources that inform the book in Chapter One, 'Introduction: Diffracting the Technoscientific Witness.' First, as an actor-network theorist, Quinlan considers the SAEK itself as one of the many actors operating in a medicolegal network which responds to sexual assault, and she argues that the kit serves as a 'boundary object' that not only coordinates the action of participants in the network, but also reflects the tensions and contradictions between medical, legal, scientific and advocacy practices.Second, situating her analysis squarely in feminist technoscience studies, Quinlan utilises Haraway's diffraction metaphor.Emphasising the multiple narratives operative in the SAEK and its history, Quinlan writes, 'Uncertainties, tensions, and debates in law, feminism, and forensic science become visible through this diffracted visioning of the kit, which lays the necessary ground for imagining more ethical and alternative ways of organising medicolegal practice around sexual assault' (21).Asking who designed it, why and how, Quinlan traces the origins of the SAEK in Chapter Two, 'Inscriptions of Doubt: Law, Anti-Rape Activism, and the Early SAEK.'She argues that early feminist critiques of how victims of sexual assault were treated by medical and legal institutions set the stage for the development of the kit.In addition to charges of pervasive sexism and misogyny in medical and legal practices, anti-rape activists criticised the lack of protocols for evidence collection and the lack of training for physicians and nurses.However, while the move to standardised evidence collection promised the elimination of bias against victims, it came at the cost of marginalising rape crisis centers and the expertise that feminist activists had developed regarding victim advocacy.It also reinforced popular distrust of women's reports of rape and demands for corroborative evidence.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.016 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".