Sexual Violence on Campus: No Evidence that Studies Are Biased Due to Self-Selection
Bibliographic record
Abstract
Numerous research studies suggest that at least one in five female college students is sexually assaulted while enrolled. However, many studies exploring sexual violence prevalence on campus use methodology permitting students to self-select into the study based on interest in the topic (i.e., students receive an email offering them the opportunity to participate in a study on sexual violence). Self-selection may bias these prevalence estimates of campus sexual violence. To explore this issue, we surveyed two samples of college women on their experiences of sexual assault. We recruited Sample 1 in a typical way: by emailing a randomly selected subset of students provided by the university registrar and inviting participation with information about the survey topic. We recruited Sample 2 using a human subjects pool where students in introductory psychology and linguistics courses sign up for studies without prior knowledge about the topic of the research they will participate in (hence greatly minimizing the risk of self-selection). The two samples yielded nearly identical victimization rates. Over a quarter of participants in both our samples had experienced sexual contact without consent, consistent with recent research from the Association of American Universities. College victimization estimates do not appear to be biased by self-selection based on knowledge of the survey topic.
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.
How this classification was reachedexpand
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".