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
Science plays a substantial, though under-acknowledged, role in shaping popular understandings of rape. Statistical figures like "1 in 4 women have experienced completed or attempted rape" are central for raising awareness. Yet such scientific facts often become points of controversy, particularly as conservative scholars and public figures attempt to discredit feminist activists. Rape by the Numbers explores scientists' approaches to studying rape over more than forty years in the United States and Canada. In addition to investigating how scientists come to know the scope, causes, and consequences of rape, this book delves into the politics of rape research. Scholars who study rape often face a range of social pressures and resource constraints, including some that are unique to feminized and politicized fields of inquiry. Collectively, these matters have far-reaching consequences. Scientific projects may determine who counts as a potential victim/survivor or aggressor in a range of contexts, shaping research agendas as well as state policy, anti-violence programming and services, and public perceptions. Social processes within the study of rape determine which knowledges count as credible science, and thus who may count as an expert in academic and public contexts.
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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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