Bibliographic record
Abstract
College campus-based surveys of sexual assault in the United States have generated one of the most high-profile and contentious figures in the history of social science: the ‘1 in 5’ statistic. Referring to the number of women who have experienced either attempted or completed sexual assault since their time in college, ‘1 in 5’ has done significant work in making the prevalence of this experience legible to the public and to policy-makers. Here I examine how sexual assault surveys have participated in structuring the ontology of date/acquaintance rape from the 1980s to today. I review the foundational work of feminist social scientists Diana Russell and Mary Koss, with particular attention to the methodological practices through which the concept of the ‘hidden’ or ‘unacknowledged’ rape victim emerged. I then examine a selection of early 21st-century sexual assault surveys and highlight the ongoing preoccupation with survey methodology in responses to their results. I argue that the survey itself has been a central actor in the ontological politics of sexual assault, and only by closely attending to its performativity can we understand the paradoxical persistence both of critical responses to the ‘1 in 5’ statistic and of its effective deployment in anti-violence policy.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".