Self-Reports of Sexual Violence Outside of Survey Reference Periods: Implications for Measurement
Bibliographic record
Abstract
Accurate measurement of sexual violence (SV) victimization is important for informing research, policy, and service provision. Measures such as the Sexual Experiences Survey (SES) that use behaviorally specific language and a specified reference period (e.g., since age 14, over the past 12 months) are considered best practice and have substantially improved SV estimates given that so few incidents are reported to police. However, to date, we know little about whether estimates are affected by respondents' reporting of incidents that occurred outside of the specified reference period (i.e., reference period errors). The current study explored the extent, nature, and impact on incidence estimates of reference period errors in two large, diverse samples of post-secondary students. Secondary analysis was conducted of data gathered using a follow-up date question after the Sexual Experiences Survey-Short Form Victimization. Between 8% and 68% of rape and attempted rape victims made reference period errors, with the highest proportion of errors occurring in the survey with the shortest reference period (1 month). These errors caused minor to moderate changes in time period-specific incidence estimates (i.e., excluding respondents with errors reduced estimates by up to 7%). Although including a date question does not guarantee that all time period-related errors will be identified, it can improve the accuracy of SV estimates, which is crucial for informing policy and prevention. Researchers measuring SV within specific reference periods should consider collecting dates of reported incidents as best practice.
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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 | 0.200 | 0.537 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".