Evaluating the One-in-Five Statistic: Women’s Risk of Sexual Assault While in College
Bibliographic record
Abstract
In 2014, U.S. president Barack Obama announced a White House Task Force to Protect Students From Sexual Assault, noting that "1 in 5 women on college campuses has been sexually assaulted during their time there." Since then, this one-in-five statistic has permeated public discourse. It is frequently reported, but some commentators have criticized it as exaggerated. Here, we address the question, "What percentage of women are sexually assaulted while in college?" After discussing definitions of sexual assault, we systematically review available data, focusing on studies that used large, representative samples of female undergraduates and multiple behaviorally specific questions. We conclude that one in five is a reasonably accurate average across women and campuses. We also review studies that are inappropriately cited as either supporting or debunking the one-in-five statistic; we explain why they do not adequately address this question. We identify and evaluate several assumptions implicit in the public discourse (e.g., the assumption that college students are at greater risk than nonstudents). Given the empirical support for the one-in-five statistic, we suggest that the controversy occurs because of misunderstandings about studies' methods and results and because this topic has implications for gender relations, power, and sexuality; this controversy is ultimately about values.
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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 | 0.025 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".