Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it