Consensual Victim–Perpetrator Intercourse after Nonconsensual Sex: The Impact of Prior Relationship
Bibliographic record
Abstract
Some female victims of nonconsensual sex subsequently have consensual sexual intercourse with the perpetrator and are more likely to do so if intercourse occurred during the nonconsensual sex than if it did not. Some evolutionary psychologists have postulated that there is something significant about nonconsensual intercourse that causes women to subsequently have a sexual relationship with the perpetrator (e.g., risk of pregnancy). In this study, we investigated a parsimonious explanation that has previously been overlooked: Intercourse is more likely during nonconsensual sex when the victim and perpetrator have previously had a sexual relationship; thus, subsequent consensual intercourse may simply be a continuation of that prior relationship. A sample of 945 women completed an Internet-based survey, of whom 41% had experienced nonconsensual sex since age 14. As expected, victims who had intercourse with perpetrators prior to the nonconsensual sex event were significantly more likely than other victims to experience nonconsensual intercourse and to engage in subsequent consensual intercourse with the perpetrator. When considering only the small subsample of victims who never had a prior romantic or sexual relationship with the perpetrator, the odds of subsequent consensual intercourse were still significantly greater following nonconsensual sex with intercourse versus without intercourse.
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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 | 0.003 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".